Failure dynamics of the global risk network.

PubMed

Szymanski, Boleslaw K; Lin, Xin; Asztalos, Andrea; Sreenivasan, Sameet

2015-06-18

Risks threatening modern societies form an intricately interconnected network that often underlies crisis situations. Yet, little is known about how risk materializations in distinct domains influence each other. Here we present an approach in which expert assessments of likelihoods and influence of risks underlie a quantitative model of the global risk network dynamics. The modeled risks range from environmental to economic and technological, and include difficult to quantify risks, such as geo-political and social. Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data. We analyze the model dynamics and study its resilience and stability. Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability. The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network.

An integrated environmental modeling framework for performing Quantitative Microbial Risk Assessments

EPA Science Inventory

Standardized methods are often used to assess the likelihood of a human-health effect from exposure to a specified hazard, and inform opinions and decisions about risk management and communication. A Quantitative Microbial Risk Assessment (QMRA) is specifically adapted to detail ...

77 FR 41985 - Use of Influenza Disease Models To Quantitatively Evaluate the Benefits and Risks of Vaccines: A...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-17

... Evaluation and Research (CBER) and suggestions for further development. The public workshop will include... Evaluation and Research (HFM-210), Food and Drug Administration, 1401 Rockville Pike, suite 200N, Rockville... models to generate quantitative estimates of the benefits and risks of influenza vaccination. The public...

An integrated environmental modeling framework for performing quantitative microbial risk assessments

USDA-ARS?s Scientific Manuscript database

Standardized methods are often used to assess the likelihood of a human-health effect from exposure to a specified hazard, and inform opinions and decisions about risk management and communication. A Quantitative Microbial Risk Assessment (QMRA) is specifically adapted to detail potential human-heal...

EVALUATING TOOLS AND MODELS USED FOR QUANTITATIVE EXTRAPOLATION OF IN VITRO TO IN VIVO DATA FOR NEUROTOXICANTS*

EPA Science Inventory

There are a number of risk management decisions, which range from prioritization for testing to quantitative risk assessments. The utility of in vitro studies in these decisions depends on how well the results of such data can be qualitatively and quantitatively extrapolated to i...

The linearized multistage model and the future of quantitative risk assessment.

PubMed

Crump, K S

1996-10-01

The linearized multistage (LMS) model has for over 15 years been the default dose-response model used by the U.S. Environmental Protection Agency (USEPA) and other federal and state regulatory agencies in the United States for calculating quantitative estimates of low-dose carcinogenic risks from animal data. The LMS model is in essence a flexible statistical model that can describe both linear and non-linear dose-response patterns, and that produces an upper confidence bound on the linear low-dose slope of the dose-response curve. Unlike its namesake, the Armitage-Doll multistage model, the parameters of the LMS do not correspond to actual physiological phenomena. Thus the LMS is 'biological' only to the extent that the true biological dose response is linear at low dose and that low-dose slope is reflected in the experimental data. If the true dose response is non-linear the LMS upper bound may overestimate the true risk by many orders of magnitude. However, competing low-dose extrapolation models, including those derived from 'biologically-based models' that are capable of incorporating additional biological information, have not shown evidence to date of being able to produce quantitative estimates of low-dose risks that are any more accurate than those obtained from the LMS model. Further, even if these attempts were successful, the extent to which more accurate estimates of low-dose risks in a test animal species would translate into improved estimates of human risk is questionable. Thus, it does not appear possible at present to develop a quantitative approach that would be generally applicable and that would offer significant improvements upon the crude bounding estimates of the type provided by the LMS model. Draft USEPA guidelines for cancer risk assessment incorporate an approach similar to the LMS for carcinogens having a linear mode of action. However, under these guidelines quantitative estimates of low-dose risks would not be developed for carcinogens having a non-linear mode of action; instead dose-response modelling would be used in the experimental range to calculate an LED10* (a statistical lower bound on the dose corresponding to a 10% increase in risk), and safety factors would be applied to the LED10* to determine acceptable exposure levels for humans. This approach is very similar to the one presently used by USEPA for non-carcinogens. Rather than using one approach for carcinogens believed to have a linear mode of action and a different approach for all other health effects, it is suggested herein that it would be more appropriate to use an approach conceptually similar to the 'LED10*-safety factor' approach for all health effects, and not to routinely develop quantitative risk estimates from animal data.

76 FR 28819 - NUREG/CR-XXXX, Development of Quantitative Software Reliability Models for Digital Protection...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-18

... NUCLEAR REGULATORY COMMISSION [NRC-2011-0109] NUREG/CR-XXXX, Development of Quantitative Software..., ``Development of Quantitative Software Reliability Models for Digital Protection Systems of Nuclear Power Plants... of Risk Analysis, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission...

Source-to-Outcome Microbial Exposure and Risk Modeling Framework

EPA Science Inventory

A Quantitative Microbial Risk Assessment (QMRA) is a computer-based data-delivery and modeling approach that integrates interdisciplinary fate/transport, exposure, and impact models and databases to characterize potential health impacts/risks due to pathogens. As such, a QMRA ex...

A Team Mental Model Perspective of Pre-Quantitative Risk

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.

2011-01-01

This study was conducted to better understand how teams conceptualize risk before it can be quantified, and the processes by which a team forms a shared mental model of this pre-quantitative risk. Using an extreme case, this study analyzes seven months of team meeting transcripts, covering the entire lifetime of the team. Through an analysis of team discussions, a rich and varied structural model of risk emerges that goes significantly beyond classical representations of risk as the product of a negative consequence and a probability. In addition to those two fundamental components, the team conceptualization includes the ability to influence outcomes and probabilities, networks of goals, interaction effects, and qualitative judgments about the acceptability of risk, all affected by associated uncertainties. In moving from individual to team mental models, team members employ a number of strategies to gain group recognition of risks and to resolve or accept differences.

Quantitative prediction of oral cancer risk in patients with oral leukoplakia.

PubMed

Liu, Yao; Li, Yicheng; Fu, Yue; Liu, Tong; Liu, Xiaoyong; Zhang, Xinyan; Fu, Jie; Guan, Xiaobing; Chen, Tong; Chen, Xiaoxin; Sun, Zheng

2017-07-11

Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.

Evaluating a multi-criteria model for hazard assessment in urban design. The Porto Marghera case study

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

Luria, Paolo; Aspinall, Peter A

2003-08-01

The aim of this paper is to describe a new approach to major industrial hazard assessment, which has been recently studied by the authors in conjunction with the Italian Environmental Protection Agency ('ARPAV'). The real opportunity for developing a different approach arose from the need of the Italian EPA to provide the Venice Port Authority with an appropriate estimation of major industrial hazards in Porto Marghera, an industrial estate near Venice (Italy). However, the standard model, the quantitative risk analysis (QRA), only provided a list of individual quantitative risk values, related to single locations. The experimental model is based onmore » a multi-criteria approach--the Analytic Hierarchy Process--which introduces the use of expert opinions, complementary skills and expertise from different disciplines in conjunction with quantitative traditional analysis. This permitted the generation of quantitative data on risk assessment from a series of qualitative assessments, on the present situation and on three other future scenarios, and use of this information as indirect quantitative measures, which could be aggregated for obtaining the global risk rate. This approach is in line with the main concepts proposed by the last European directive on Major Hazard Accidents, which recommends increasing the participation of operators, taking the other players into account and, moreover, paying more attention to the concepts of 'urban control', 'subjective risk' (risk perception) and intangible factors (factors not directly quantifiable)« less

How to make predictions about future infectious disease risks

PubMed Central

Woolhouse, Mark

2011-01-01

Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

Consumers' behavior in quantitative microbial risk assessment for pathogens in raw milk: Incorporation of the likelihood of consumption as a function of storage time and temperature.

PubMed

Crotta, Matteo; Paterlini, Franco; Rizzi, Rita; Guitian, Javier

2016-02-01

Foodborne disease as a result of raw milk consumption is an increasing concern in Western countries. Quantitative microbial risk assessment models have been used to estimate the risk of illness due to different pathogens in raw milk. In these models, the duration and temperature of storage before consumption have a critical influence in the final outcome of the simulations and are usually described and modeled as independent distributions in the consumer phase module. We hypothesize that this assumption can result in the computation, during simulations, of extreme scenarios that ultimately lead to an overestimation of the risk. In this study, a sensorial analysis was conducted to replicate consumers' behavior. The results of the analysis were used to establish, by means of a logistic model, the relationship between time-temperature combinations and the probability that a serving of raw milk is actually consumed. To assess our hypothesis, 2 recently published quantitative microbial risk assessment models quantifying the risks of listeriosis and salmonellosis related to the consumption of raw milk were implemented. First, the default settings described in the publications were kept; second, the likelihood of consumption as a function of the length and temperature of storage was included. When results were compared, the density of computed extreme scenarios decreased significantly in the modified model; consequently, the probability of illness and the expected number of cases per year also decreased. Reductions of 11.6 and 12.7% in the proportion of computed scenarios in which a contaminated milk serving was consumed were observed for the first and the second study, respectively. Our results confirm that overlooking the time-temperature dependency may yield to an important overestimation of the risk. Furthermore, we provide estimates of this dependency that could easily be implemented in future quantitative microbial risk assessment models of raw milk pathogens. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Applying quantitative benefit-risk analysis to aid regulatory decision making in diagnostic imaging: methods, challenges, and opportunities.

PubMed

Agapova, Maria; Devine, Emily Beth; Bresnahan, Brian W; Higashi, Mitchell K; Garrison, Louis P

2014-09-01

Health agencies making regulatory marketing-authorization decisions use qualitative and quantitative approaches to assess expected benefits and expected risks associated with medical interventions. There is, however, no universal standard approach that regulatory agencies consistently use to conduct benefit-risk assessment (BRA) for pharmaceuticals or medical devices, including for imaging technologies. Economics, health services research, and health outcomes research use quantitative approaches to elicit preferences of stakeholders, identify priorities, and model health conditions and health intervention effects. Challenges to BRA in medical devices are outlined, highlighting additional barriers in radiology. Three quantitative methods--multi-criteria decision analysis, health outcomes modeling and stated-choice survey--are assessed using criteria that are important in balancing benefits and risks of medical devices and imaging technologies. To be useful in regulatory BRA, quantitative methods need to: aggregate multiple benefits and risks, incorporate qualitative considerations, account for uncertainty, and make clear whose preferences/priorities are being used. Each quantitative method performs differently across these criteria and little is known about how BRA estimates and conclusions vary by approach. While no specific quantitative method is likely to be the strongest in all of the important areas, quantitative methods may have a place in BRA of medical devices and radiology. Quantitative BRA approaches have been more widely applied in medicines, with fewer BRAs in devices. Despite substantial differences in characteristics of pharmaceuticals and devices, BRA methods may be as applicable to medical devices and imaging technologies as they are to pharmaceuticals. Further research to guide the development and selection of quantitative BRA methods for medical devices and imaging technologies is needed. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies.

PubMed

Torres-Mejía, Gabriela; De Stavola, Bianca; Allen, Diane S; Pérez-Gavilán, Juan J; Ferreira, Jorge M; Fentiman, Ian S; Dos Santos Silva, Isabel

2005-05-01

Mammographic features are known to be associated with breast cancer but the magnitude of the effect differs markedly from study to study. Methods to assess mammographic features range from subjective qualitative classifications to computer-automated quantitative measures. We used data from the UK Guernsey prospective studies to examine the relative value of these methods in predicting breast cancer risk. In all, 3,211 women ages > or =35 years who had a mammogram taken in 1986 to 1989 were followed-up to the end of October 2003, with 111 developing breast cancer during this period. Mammograms were classified using the subjective qualitative Wolfe classification and several quantitative mammographic features measured using computer-based techniques. Breast cancer risk was positively associated with high-grade Wolfe classification, percent breast density and area of dense tissue, and negatively associated with area of lucent tissue, fractal dimension, and lacunarity. Inclusion of the quantitative measures in the same model identified area of dense tissue and lacunarity as the best predictors of breast cancer, with risk increasing by 59% [95% confidence interval (95% CI), 29-94%] per SD increase in total area of dense tissue but declining by 39% (95% CI, 53-22%) per SD increase in lacunarity, after adjusting for each other and for other confounders. Comparison of models that included both the qualitative Wolfe classification and these two quantitative measures to models that included either the qualitative or the two quantitative variables showed that they all made significant contributions to prediction of breast cancer risk. These findings indicate that breast cancer risk is affected not only by the amount of mammographic density but also by the degree of heterogeneity of the parenchymal pattern and, presumably, by other features captured by the Wolfe classification.

Quantitative Microbial Risk Assessment and Infectious Disease Transmission Modeling of Waterborne Enteric Pathogens.

PubMed

Brouwer, Andrew F; Masters, Nina B; Eisenberg, Joseph N S

2018-04-20

Waterborne enteric pathogens remain a global health threat. Increasingly, quantitative microbial risk assessment (QMRA) and infectious disease transmission modeling (IDTM) are used to assess waterborne pathogen risks and evaluate mitigation. These modeling efforts, however, have largely been conducted independently for different purposes and in different settings. In this review, we examine the settings where each modeling strategy is employed. QMRA research has focused on food contamination and recreational water in high-income countries (HICs) and drinking water and wastewater in low- and middle-income countries (LMICs). IDTM research has focused on large outbreaks (predominately LMICs) and vaccine-preventable diseases (LMICs and HICs). Human ecology determines the niches that pathogens exploit, leading researchers to focus on different risk assessment research strategies in different settings. To enhance risk modeling, QMRA and IDTM approaches should be integrated to include dynamics of pathogens in the environment and pathogen transmission through populations.

QUANTITATIVE PROCEDURES FOR NEUROTOXICOLOGY RISK ASSESSMENT

EPA Science Inventory

In this project, previously published information on biologically based dose-response model for brain development was used to quantitatively evaluate critical neurodevelopmental processes, and to assess potential chemical impacts on early brain development. This model has been ex...

Quantitative assessment of human health risk posed by polycyclic aromatic hydrocarbons in urban road dust.

PubMed

Ma, Yukun; Liu, An; Egodawatta, Prasanna; McGree, James; Goonetilleke, Ashantha

2017-01-01

Among the numerous pollutants present in urban road dust, polycyclic aromatic hydrocarbons (PAHs) are among the most toxic chemical pollutants and can pose cancer risk to humans. The primary aim of the study was to develop a quantitative model to assess the cancer risk from PAHs in urban road dust based on traffic and land use factors and thereby to characterise the risk posed by PAHs in fine (<150μm) and coarse (>150μm) particles. The risk posed by PAHs was quantified as incremental lifetime cancer risk (ILCR), which was modelled as a function of traffic volume and percentages of different urban land uses. The study outcomes highlighted the fact that cancer risk from PAHs in urban road dust is primarily influenced by PAHs associated with fine solids. Heavy PAHs with 5 to 6 benzene rings, especially dibenzo[a,h]anthracene (D[a]A) and benzo[a]pyrene (B[a]P) in the mixture contribute most to the risk. The quantitative model developed based on traffic and land use factors will contribute to informed decision making in relation to the management of risk posed by PAHs in urban road dust. Copyright © 2016 Elsevier B.V. All rights reserved.

Development of a semi-quantitative risk assessment model for evaluating environmental threat posed by the three first EU watch-list pharmaceuticals to urban wastewater treatment plants: An Irish case study.

PubMed

Tahar, Alexandre; Tiedeken, Erin Jo; Clifford, Eoghan; Cummins, Enda; Rowan, Neil

2017-12-15

Contamination of receiving waters with pharmaceutical compounds is of pressing concern. This constitutes the first study to report on the development of a semi-quantitative risk assessment (RA) model for evaluating the environmental threat posed by three EU watch list pharmaceutical compounds namely, diclofenac, 17-beta-estradiol and 17-alpha-ethinylestradiol, to aquatic ecosystems using Irish data as a case study. This RA model adopts the Irish Environmental Protection Agency Source-Pathway-Receptor concept to define relevant parameters for calculating low, medium or high risk score for each agglomeration of wastewater treatment plant (WWTP), which include catchment, treatments, operational and management factors. This RA model may potentially be used on a national scale to (i) identify WWTPs that pose a particular risk as regards releasing disproportionally high levels of these pharmaceutical compounds, and (ii) help identify priority locations for introducing or upgrading control measures (e.g. tertiary treatment, source reduction). To assess risks for these substances of emerging concern, the model was applied to 16 urban WWTPs located in different regions in Ireland that were scored for the three different compounds and ranked as low, medium or high risk. As a validation proxy, this case study used limited monitoring data recorded at some these plants receiving waters. It is envisaged that this semi-quantitative RA approach may aid other EU countries investigate and screen for potential risks where limited measured or predicted environmental pollutant concentrations and/or hydrological data are available. This model is semi-quantitative, as other factors such as influence of climate change and drug usage or prescription data will need to be considered in a future point for estimating and predicting risks. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative assessment of the microbial risk of leafy greens from farm to consumption: preliminary framework, data, and risk estimates.

PubMed

Danyluk, Michelle D; Schaffner, Donald W

2011-05-01

This project was undertaken to relate what is known about the behavior of Escherichia coli O157:H7 under laboratory conditions and integrate this information to what is known regarding the 2006 E. coli O157:H7 spinach outbreak in the context of a quantitative microbial risk assessment. The risk model explicitly assumes that all contamination arises from exposure in the field. Extracted data, models, and user inputs were entered into an Excel spreadsheet, and the modeling software @RISK was used to perform Monte Carlo simulations. The model predicts that cut leafy greens that are temperature abused will support the growth of E. coli O157:H7, and populations of the organism may increase by as much a 1 log CFU/day under optimal temperature conditions. When the risk model used a starting level of -1 log CFU/g, with 0.1% of incoming servings contaminated, the predicted numbers of cells per serving were within the range of best available estimates of pathogen levels during the outbreak. The model predicts that levels in the field of -1 log CFU/g and 0.1% prevalence could have resulted in an outbreak approximately the size of the 2006 E. coli O157:H7 outbreak. This quantitative microbial risk assessment model represents a preliminary framework that identifies available data and provides initial risk estimates for pathogenic E. coli in leafy greens. Data gaps include retail storage times, correlations between storage time and temperature, determining the importance of E. coli O157:H7 in leafy greens lag time models, and validation of the importance of cross-contamination during the washing process.

Studying Biology to Understand Risk: Dosimetry Models and Quantitative Adverse Outcome Pathways

EPA Science Inventory

Confidence in the quantitative prediction of risk is increased when the prediction is based to as great an extent as possible on the relevant biological factors that constitute the pathway from exposure to adverse outcome. With the first examples now over 40 years old, physiologi...

Flightdeck Automation Problems (FLAP) Model for Safety Technology Portfolio Assessment

NASA Technical Reports Server (NTRS)

Ancel, Ersin; Shih, Ann T.

2014-01-01

NASA's Aviation Safety Program (AvSP) develops and advances methodologies and technologies to improve air transportation safety. The Safety Analysis and Integration Team (SAIT) conducts a safety technology portfolio assessment (PA) to analyze the program content, to examine the benefits and risks of products with respect to program goals, and to support programmatic decision making. The PA process includes systematic identification of current and future safety risks as well as tracking several quantitative and qualitative metrics to ensure the program goals are addressing prominent safety risks accurately and effectively. One of the metrics within the PA process involves using quantitative aviation safety models to gauge the impact of the safety products. This paper demonstrates the role of aviation safety modeling by providing model outputs and evaluating a sample of portfolio elements using the Flightdeck Automation Problems (FLAP) model. The model enables not only ranking of the quantitative relative risk reduction impact of all portfolio elements, but also highlighting the areas with high potential impact via sensitivity and gap analyses in support of the program office. Although the model outputs are preliminary and products are notional, the process shown in this paper is essential to a comprehensive PA of NASA's safety products in the current program and future programs/projects.

Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection

NASA Astrophysics Data System (ADS)

Kawata, Y.; Niki, N.; Ohmatsu, H.; Satake, M.; Kusumoto, M.; Tsuchida, T.; Aokage, K.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2014-03-01

In this work, we investigate a potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection. The elucidation of the subcategorization of a pulmonary nodule type in CT images is an important preliminary step towards developing the nodule managements that are specific to each patient. We categorize lung cancers by analyzing volumetric distributions of CT values within lung cancers via a topic model such as latent Dirichlet allocation. Through applying our scheme to 3D CT images of nonsmall- cell lung cancer (maximum lesion size of 3 cm) , we demonstrate the potential usefulness of the topic model-based categorization of lung cancers as quantitative CT biomarkers.

Quantitative influence of risk factors on blood glucose level.

PubMed

Chen, Songjing; Luo, Senlin; Pan, Limin; Zhang, Tiemei; Han, Longfei; Zhao, Haixiu

2014-01-01

The aim of this study is to quantitatively analyze the influence of risk factors on the blood glucose level, and to provide theory basis for understanding the characteristics of blood glucose change and confirming the intervention index for type 2 diabetes. The quantitative method is proposed to analyze the influence of risk factors on blood glucose using back propagation (BP) neural network. Ten risk factors are screened first. Then the cohort is divided into nine groups by gender and age. According to the minimum error principle, nine BP models are trained respectively. The quantitative values of the influence of different risk factors on the blood glucose change can be obtained by sensitivity calculation. The experiment results indicate that weight is the leading cause of blood glucose change (0.2449). The second factors are cholesterol, age and triglyceride. The total ratio of these four factors reaches to 77% of the nine screened risk factors. And the sensitivity sequences can provide judgment method for individual intervention. This method can be applied to risk factors quantitative analysis of other diseases and potentially used for clinical practitioners to identify high risk populations for type 2 diabetes as well as other disease.

Quantitative Method for Analyzing the Allocation of Risks in Transportation Construction

DOT National Transportation Integrated Search

1979-04-01

The report presents a conceptual model of risk that was developed to analyze the impact on owner's cost of alternate allocations of risk among owner and contractor in mass transit construction. A model and analysis procedure are developed, based on d...

[Quantitative risk model for verocytotoxigenic Escherichia coli cross-contamination during homemade hamburger preparation].

PubMed

Signorini, M L; Frizzo, L S

2009-01-01

The objective of this study was to develop a quantitative risk model for verocytotoxigenic Escherichia coil (VTEC) cross-contamination during hamburger preparation at home. Published scientific information about the disease was considered for the elaboration of the model, which included a number of routines performed during food preparation in kitchens. The associated probabilities of bacterial transference between food items and kitchen utensils which best described each stage of the process were incorporated into the model by using @Risk software. Handling raw meat before preparing ready-to-eat foods (Odds ratio, OR, 6.57), as well as hand (OR = 12.02) and cutting board (OR = 5.02) washing habits were the major risk factors of VTEC cross-contamination from meat to vegetables. The information provided by this model should be considered when designing public information campaigns on hemolytic uremic syndrome risk directed to food handlers, in order to stress the importance of the above mentioned factors in disease transmission.

Project risk management in the construction of high-rise buildings

NASA Astrophysics Data System (ADS)

Titarenko, Boris; Hasnaoui, Amir; Titarenko, Roman; Buzuk, Liliya

2018-03-01

This paper shows the project risk management methods, which allow to better identify risks in the construction of high-rise buildings and to manage them throughout the life cycle of the project. One of the project risk management processes is a quantitative analysis of risks. The quantitative analysis usually includes the assessment of the potential impact of project risks and their probabilities. This paper shows the most popular methods of risk probability assessment and tries to indicate the advantages of the robust approach over the traditional methods. Within the framework of the project risk management model a robust approach of P. Huber is applied and expanded for the tasks of regression analysis of project data. The suggested algorithms used to assess the parameters in statistical models allow to obtain reliable estimates. A review of the theoretical problems of the development of robust models built on the methodology of the minimax estimates was done and the algorithm for the situation of asymmetric "contamination" was developed.

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.

Proceedings of the Conference on Toxicology: Applications of Advances in Toxicology to Risk Assessment. Held at Wright-Patterson AFB, Ohio on 19-21 May 1992

DTIC Science & Technology

1993-01-01

animals in toxicology research, the application of pharmacokinetics and physiologically based pharmacokinetic mdels in chemical risk assessment, selected...metaplasia Neurotoxicity Nonmutagenic carcinogens Ozone P450 PBPK modeling Perfluorohexane Peroxisome proliferators Pharmacokinetics Pharmacokinetic models...Physiological modeling Physiologically based pharmacokinetic modeling Polycyclic organic matter Quantitative risk assessment RAIRM model Rats

Security Events and Vulnerability Data for Cybersecurity Risk Estimation.

PubMed

Allodi, Luca; Massacci, Fabio

2017-08-01

Current industry standards for estimating cybersecurity risk are based on qualitative risk matrices as opposed to quantitative risk estimates. In contrast, risk assessment in most other industry sectors aims at deriving quantitative risk estimations (e.g., Basel II in Finance). This article presents a model and methodology to leverage on the large amount of data available from the IT infrastructure of an organization's security operation center to quantitatively estimate the probability of attack. Our methodology specifically addresses untargeted attacks delivered by automatic tools that make up the vast majority of attacks in the wild against users and organizations. We consider two-stage attacks whereby the attacker first breaches an Internet-facing system, and then escalates the attack to internal systems by exploiting local vulnerabilities in the target. Our methodology factors in the power of the attacker as the number of "weaponized" vulnerabilities he/she can exploit, and can be adjusted to match the risk appetite of the organization. We illustrate our methodology by using data from a large financial institution, and discuss the significant mismatch between traditional qualitative risk assessments and our quantitative approach. © 2017 Society for Risk Analysis.

Emerging Infectious Diseases and Blood Safety: Modeling the Transfusion-Transmission Risk.

PubMed

Kiely, Philip; Gambhir, Manoj; Cheng, Allen C; McQuilten, Zoe K; Seed, Clive R; Wood, Erica M

2017-07-01

While the transfusion-transmission (TT) risk associated with the major transfusion-relevant viruses such as HIV is now very low, during the last 20 years there has been a growing awareness of the threat to blood safety from emerging infectious diseases, a number of which are known to be, or are potentially, transfusion transmissible. Two published models for estimating the transfusion-transmission risk from EIDs, referred to as the Biggerstaff-Petersen model and the European Upfront Risk Assessment Tool (EUFRAT), respectively, have been applied to several EIDs in outbreak situations. We describe and compare the methodological principles of both models, highlighting their similarities and differences. We also discuss the appropriateness of comparing results from the two models. Quantitating the TT risk of EIDs can inform decisions about risk mitigation strategies and their cost-effectiveness. Finally, we present a qualitative risk assessment for Zika virus (ZIKV), an EID agent that has caused several outbreaks since 2007. In the latest and largest ever outbreak, several probable cases of transfusion-transmission ZIKV have been reported, indicating that it is transfusion-transmissible and therefore a risk to blood safety. We discuss why quantitative modeling the TT risk of ZIKV is currently problematic. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

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.

Quantitative Risk Assessment of Human Trichinellosis Caused by Consumption of Pork Meat Sausages in Argentina.

PubMed

Sequeira, G J; Zbrun, M V; Soto, L P; Astesana, D M; Blajman, J E; Rosmini, M R; Frizzo, L S; Signorini, M L

2016-03-01

In Argentina, there are three known species of genus Trichinella; however, Trichinella spiralis is most commonly associated with domestic pigs and it is recognized as the main cause of human trichinellosis by the consumption of products made with raw or insufficiently cooked pork meat. In some areas of Argentina, this disease is endemic and it is thus necessary to develop a more effective programme of prevention and control. Here, we developed a quantitative risk assessment of human trichinellosis following pork meat sausage consumption, which may be used to identify the stages with greater impact on the probability of acquiring the disease. The quantitative model was designed to describe the conditions in which the meat is produced, processed, transported, stored, sold and consumed in Argentina. The model predicted a risk of human trichinellosis of 4.88 × 10(-6) and an estimated annual number of trichinellosis cases of 109. The risk of human trichinellosis was sensitive to the number of Trichinella larvae that effectively survived the storage period (r = 0.89), the average probability of infection (PPinf ) (r = 0.44) and the storage time (Storage) (r = 0.08). This model allowed assessing the impact of different factors influencing the risk of acquiring trichinellosis. The model may thus help to select possible strategies to reduce the risk in the chain of by-products of pork production. © 2015 Blackwell Verlag GmbH.

EVALUATION OF PHYSIOLOGY COMPUTER MODELS, AND THE FEASIBILITY OF THEIR USE IN RISK ASSESSMENT.

EPA Science Inventory

This project will evaluate the current state of quantitative models that simulate physiological processes, and the how these models might be used in conjunction with the current use of PBPK and BBDR models in risk assessment. The work will include a literature search to identify...

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

PubMed

Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A

2018-05-01

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

Integration of PKPD relationships into benefit–risk analysis

PubMed Central

Bellanti, Francesco; van Wijk, Rob C; Danhof, Meindert; Della Pasqua, Oscar

2015-01-01

Aim Despite the continuous endeavour to achieve high standards in medical care through effectiveness measures, a quantitative framework for the assessment of the benefit–risk balance of new medicines is lacking prior to regulatory approval. The aim of this short review is to summarise the approaches currently available for benefit–risk assessment. In addition, we propose the use of pharmacokinetic–pharmacodynamic (PKPD) modelling as the pharmacological basis for evidence synthesis and evaluation of novel therapeutic agents. Methods A comprehensive literature search has been performed using MESH terms in PubMed, in which articles describing benefit–risk assessment and modelling and simulation were identified. In parallel, a critical review of multi-criteria decision analysis (MCDA) is presented as a tool for characterising a drug's safety and efficacy profile. Results A definition of benefits and risks has been proposed by the European Medicines Agency (EMA), in which qualitative and quantitative elements are included. However, in spite of the value of MCDA as a quantitative method, decisions about benefit–risk balance continue to rely on subjective expert opinion. By contrast, a model-informed approach offers the opportunity for a more comprehensive evaluation of benefit–risk balance before extensive evidence is generated in clinical practice. Conclusions Benefit–risk balance should be an integral part of the risk management plan and as such considered before marketing authorisation. Modelling and simulation can be incorporated into MCDA to support the evidence synthesis as well evidence generation taking into account the underlying correlations between favourable and unfavourable effects. In addition, it represents a valuable tool for the optimization of protocol design in effectiveness trials. PMID:25940398

Integration of PKPD relationships into benefit-risk analysis.

PubMed

Bellanti, Francesco; van Wijk, Rob C; Danhof, Meindert; Della Pasqua, Oscar

2015-11-01

Despite the continuous endeavour to achieve high standards in medical care through effectiveness measures, a quantitative framework for the assessment of the benefit-risk balance of new medicines is lacking prior to regulatory approval. The aim of this short review is to summarise the approaches currently available for benefit-risk assessment. In addition, we propose the use of pharmacokinetic-pharmacodynamic (PKPD) modelling as the pharmacological basis for evidence synthesis and evaluation of novel therapeutic agents. A comprehensive literature search has been performed using MESH terms in PubMed, in which articles describing benefit-risk assessment and modelling and simulation were identified. In parallel, a critical review of multi-criteria decision analysis (MCDA) is presented as a tool for characterising a drug's safety and efficacy profile. A definition of benefits and risks has been proposed by the European Medicines Agency (EMA), in which qualitative and quantitative elements are included. However, in spite of the value of MCDA as a quantitative method, decisions about benefit-risk balance continue to rely on subjective expert opinion. By contrast, a model-informed approach offers the opportunity for a more comprehensive evaluation of benefit-risk balance before extensive evidence is generated in clinical practice. Benefit-risk balance should be an integral part of the risk management plan and as such considered before marketing authorisation. Modelling and simulation can be incorporated into MCDA to support the evidence synthesis as well evidence generation taking into account the underlying correlations between favourable and unfavourable effects. In addition, it represents a valuable tool for the optimization of protocol design in effectiveness trials. © 2015 The British Pharmacological Society.

Using an Integrated, Multi-disciplinary Framework to Support Quantitative Microbial Risk Assessments

EPA Science Inventory

The Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES) provides the infrastructure to link disparate models and databases seamlessly, giving an assessor the ability to construct an appropriate conceptual site model from a host of modeling choices, so a numbe...

Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children.

PubMed

Berchialla, Paola; Scarinzi, Cecilia; Snidero, Silvia; Gregori, Dario

2016-08-01

Risk Assessment is the systematic study of decisions subject to uncertain consequences. An increasing interest has been focused on modeling techniques like Bayesian Networks since their capability of (1) combining in the probabilistic framework different type of evidence including both expert judgments and objective data; (2) overturning previous beliefs in the light of the new information being received and (3) making predictions even with incomplete data. In this work, we proposed a comparison among Bayesian Networks and other classical Quantitative Risk Assessment techniques such as Neural Networks, Classification Trees, Random Forests and Logistic Regression models. Hybrid approaches, combining both Classification Trees and Bayesian Networks, were also considered. Among Bayesian Networks, a clear distinction between purely data-driven approach and combination of expert knowledge with objective data is made. The aim of this paper consists in evaluating among this models which best can be applied, in the framework of Quantitative Risk Assessment, to assess the safety of children who are exposed to the risk of inhalation/insertion/aspiration of consumer products. The issue of preventing injuries in children is of paramount importance, in particular where product design is involved: quantifying the risk associated to product characteristics can be of great usefulness in addressing the product safety design regulation. Data of the European Registry of Foreign Bodies Injuries formed the starting evidence for risk assessment. Results showed that Bayesian Networks appeared to have both the ease of interpretability and accuracy in making prediction, even if simpler models like logistic regression still performed well. © The Author(s) 2013.

Modeling number of bacteria per food unit in comparison to bacterial concentration in quantitative risk assessment: impact on risk estimates.

PubMed

Pouillot, Régis; Chen, Yuhuan; Hoelzer, Karin

2015-02-01

When developing quantitative risk assessment models, a fundamental consideration for risk assessors is to decide whether to evaluate changes in bacterial levels in terms of concentrations or in terms of bacterial numbers. Although modeling bacteria in terms of integer numbers may be regarded as a more intuitive and rigorous choice, modeling bacterial concentrations is more popular as it is generally less mathematically complex. We tested three different modeling approaches in a simulation study. The first approach considered bacterial concentrations; the second considered the number of bacteria in contaminated units, and the third considered the expected number of bacteria in contaminated units. Simulation results indicate that modeling concentrations tends to overestimate risk compared to modeling the number of bacteria. A sensitivity analysis using a regression tree suggests that processes which include drastic scenarios consisting of combinations of large bacterial inactivation followed by large bacterial growth frequently lead to a >10-fold overestimation of the average risk when modeling concentrations as opposed to bacterial numbers. Alternatively, the approach of modeling the expected number of bacteria in positive units generates results similar to the second method and is easier to use, thus potentially representing a promising compromise. Published by Elsevier Ltd.

Students' Mental Models with Respect to Flood Risk in the Netherlands

ERIC Educational Resources Information Center

Bosschaart, Adwin; Kuiper, Wilmad; van der Schee, Joop

2015-01-01

Until now various quantitative studies have shown that adults and students in the Netherlands have low flood risk perceptions. In this study we interviewed fifty 15-year-old students in two different flood prone areas. In order to find out how they think and reason about the risk of flooding, the mental model approach was used. Flood risk turned…

Probabilistic quantitative microbial risk assessment model of norovirus from wastewater irrigated vegetables in Ghana using genome copies and fecal indicator ratio conversion for estimating exposure dose.

PubMed

Owusu-Ansah, Emmanuel de-Graft Johnson; Sampson, Angelina; Amponsah, Samuel K; Abaidoo, Robert C; Dalsgaard, Anders; Hald, Tine

2017-12-01

The need to replace the commonly applied fecal indicator conversions ratio (an assumption of 1:10 -5 virus to fecal indicator organism) in Quantitative Microbial Risk Assessment (QMRA) with models based on quantitative data on the virus of interest has gained prominence due to the different physical and environmental factors that might influence the reliability of using indicator organisms in microbial risk assessment. The challenges facing analytical studies on virus enumeration (genome copies or particles) have contributed to the already existing lack of data in QMRA modelling. This study attempts to fit a QMRA model to genome copies of norovirus data. The model estimates the risk of norovirus infection from the intake of vegetables irrigated with wastewater from different sources. The results were compared to the results of a corresponding model using the fecal indicator conversion ratio to estimate the norovirus count. In all scenarios of using different water sources, the application of the fecal indicator conversion ratio underestimated the norovirus disease burden, measured by the Disability Adjusted Life Years (DALYs), when compared to results using the genome copies norovirus data. In some cases the difference was >2 orders of magnitude. All scenarios using genome copies met the 10 -4 DALY per person per year for consumption of vegetables irrigated with wastewater, although these results are considered to be highly conservative risk estimates. The fecal indicator conversion ratio model of stream-water and drain-water sources of wastewater achieved the 10 -6 DALY per person per year threshold, which tends to indicate an underestimation of health risk when compared to using genome copies for estimating the dose. Copyright © 2017 Elsevier B.V. All rights reserved.

Parameters for Pesticide QSAR and PBPK/PD Models to inform Human Risk Assessments

EPA Science Inventory

Physiologically-based pharmacokinetic and pharmacodynamic (PBPK/PD) modeling has emerged as an important computational approach supporting quantitative risk assessment of agrochemicals. However, before complete regulatory acceptance of this tool, an assessment of assets and liabi...

Parameters for Pyrethroid Insecticide QSAR and PBPK/PD Models for Human Risk Assessment

EPA Science Inventory

This pyrethroid insecticide parameter review is an extension of our interest in developing quantitative structure–activity relationship–physiologically based pharmacokinetic/pharmacodynamic (QSAR-PBPK/PD) models for assessing health risks, which interest started with the organoph...

Quantitative risk assessment of human salmonellosis in Canadian broiler chicken breast from retail to consumption.

PubMed

Smadi, Hanan; Sargeant, Jan M

2013-02-01

The current quantitative risk assessment model followed the framework proposed by the Codex Alimentarius to provide an estimate of the risk of human salmonellosis due to consumption of chicken breasts which were bought from Canadian retail stores and prepared in Canadian domestic kitchens. The model simulated the level of Salmonella contamination on chicken breasts throughout the retail-to-table pathway. The model used Canadian input parameter values, where available, to represent risk of salmonellosis. From retail until consumption, changes in the concentration of Salmonella on each chicken breast were modeled using equations for growth and inactivation. The model predicted an average of 318 cases of salmonellosis per 100,000 consumers per year. Potential reasons for this overestimation were discussed. A sensitivity analysis showed that concentration of Salmonella on chicken breasts at retail and food hygienic practices in private kitchens such as cross-contamination due to not washing cutting boards (or utensils) and hands after handling raw meat along with inadequate cooking contributed most significantly to the risk of human salmonellosis. The outcome from this model emphasizes that responsibility for protection from Salmonella hazard on chicken breasts is a shared responsibility. Data needed for a comprehensive Canadian Salmonella risk assessment were identified for future research. © 2012 Society for Risk Analysis.

Assessment of the Casualty Risk of Multiple Meteorological Hazards in China

PubMed Central

Xu, Wei; Zhuo, Li; Zheng, Jing; Ge, Yi; Gu, Zhihui; Tian, Yugang

2016-01-01

A study of the frequency, intensity, and risk of extreme climatic events or natural hazards is important for assessing the impacts of climate change. Many models have been developed to assess the risk of multiple hazards, however, most of the existing approaches can only model the relative levels of risk. This paper reports the development of a method for the quantitative assessment of the risk of multiple hazards based on information diffusion. This method was used to assess the risks of loss of human lives from 11 types of meteorological hazards in China at the prefectural and provincial levels. Risk curves of multiple hazards were obtained for each province and the risks of 10-year, 20-year, 50-year, and 100-year return periods were mapped. The results show that the provinces (municipalities, autonomous regions) in southeastern China are at higher risk of multiple meteorological hazards as a result of their geographical location and topography. The results of this study can be used as references for the management of meteorological disasters in China. The model can be used to quantitatively calculate the risks of casualty, direct economic losses, building collapse, and agricultural losses for any hazards at different spatial scales. PMID:26901210

Assessment of the Casualty Risk of Multiple Meteorological Hazards in China.

PubMed

Xu, Wei; Zhuo, Li; Zheng, Jing; Ge, Yi; Gu, Zhihui; Tian, Yugang

2016-02-17

A study of the frequency, intensity, and risk of extreme climatic events or natural hazards is important for assessing the impacts of climate change. Many models have been developed to assess the risk of multiple hazards, however, most of the existing approaches can only model the relative levels of risk. This paper reports the development of a method for the quantitative assessment of the risk of multiple hazards based on information diffusion. This method was used to assess the risks of loss of human lives from 11 types of meteorological hazards in China at the prefectural and provincial levels. Risk curves of multiple hazards were obtained for each province and the risks of 10-year, 20-year, 50-year, and 100-year return periods were mapped. The results show that the provinces (municipalities, autonomous regions) in southeastern China are at higher risk of multiple meteorological hazards as a result of their geographical location and topography. The results of this study can be used as references for the management of meteorological disasters in China. The model can be used to quantitatively calculate the risks of casualty, direct economic losses, building collapse, and agricultural losses for any hazards at different spatial scales.

68Ga-PSMA-617 PET/CT: a promising new technique for predicting risk stratification and metastatic risk of prostate cancer patients.

PubMed

Liu, Chen; Liu, Teli; Zhang, Ning; Liu, Yiqiang; Li, Nan; Du, Peng; Yang, Yong; Liu, Ming; Gong, Kan; Yang, Xing; Zhu, Hua; Yan, Kun; Yang, Zhi

2018-05-02

The purpose of this study was to investigate the performance of 68 Ga-PSMA-617 PET/CT in predicting risk stratification and metastatic risk of prostate cancer. Fifty newly diagnosed patients with prostate cancer as confirmed by needle biopsy were continuously included, 40 in a train set and ten in a test set. 68 Ga-PSMA-617 PET/CT and clinical data of all patients were retrospectively analyzed. Semi-quantitative analysis of PET images provided maximum standardized uptake (SUVmax) of primary prostate cancer and volumetric parameters including intraprostatic PSMA-derived tumor volume (iPSMA-TV) and intraprostatic total lesion PSMA (iTL-PSMA). According to prostate cancer risk stratification criteria of the NCCN Guideline, all patients were simplified into a low-intermediate risk group or a high-risk group. The semi-quantitative parameters of 68 Ga-PSMA-617 PET/CT were used to establish a univariate logistic regression model for high-risk prostate cancer and its metastatic risk, and to evaluate the diagnostic efficacy of the predictive model. In the train set, 30/40 (75%) patients had high-risk prostate cancer and 10/40 (25%) patients had low-to-moderate-risk prostate cancer; in the test set, 8/10 (80%) patients had high-risk prostate cancer while 2/10 (20%) had low-intermediate risk prostate cancer. The univariate logistic regression model established with SUVmax, iPSMA-TV and iTL-PSMA could all effectively predict high-risk prostate cancer; the AUC of ROC were 0.843, 0.802 and 0.900, respectively. Based on the test set, the sensitivity and specificity of each model were 87.5% and 50% for SUVmax, 62.5% and 100% for iPSMA-TV, and 87.5% and 100% for iTL-PSMA, respectively. The iPSMA-TV and iTL-PSMA-based predictive model could predict the metastatic risk of prostate cancer, the AUC of ROC was 0.863 and 0.848, respectively, but the SUVmax-based prediction model could not predict metastatic risk. Semi-quantitative analysis indexes of 68 Ga-PSMA-617 PET/CT imaging can be used as "imaging biomarkers" to predict risk stratification and metastatic risk of prostate cancer.

Evaluating variability and uncertainty separately in microbial quantitative risk assessment using two R packages.

PubMed

Pouillot, Régis; Delignette-Muller, Marie Laure

2010-09-01

Quantitative risk assessment has emerged as a valuable tool to enhance the scientific basis of regulatory decisions in the food safety domain. This article introduces the use of two new computing resources (R packages) specifically developed to help risk assessors in their projects. The first package, "fitdistrplus", gathers tools for choosing and fitting a parametric univariate distribution to a given dataset. The data may be continuous or discrete. Continuous data may be right-, left- or interval-censored as is frequently obtained with analytical methods, with the possibility of various censoring thresholds within the dataset. Bootstrap procedures then allow the assessor to evaluate and model the uncertainty around the parameters and to transfer this information into a quantitative risk assessment model. The second package, "mc2d", helps to build and study two dimensional (or second-order) Monte-Carlo simulations in which the estimation of variability and uncertainty in the risk estimates is separated. This package easily allows the transfer of separated variability and uncertainty along a chain of conditional mathematical and probabilistic models. The usefulness of these packages is illustrated through a risk assessment of hemolytic and uremic syndrome in children linked to the presence of Escherichia coli O157:H7 in ground beef. These R packages are freely available at the Comprehensive R Archive Network (cran.r-project.org). Copyright 2010 Elsevier B.V. All rights reserved.

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

NASA Astrophysics Data System (ADS)

Balbi, Stefano; Villa, Ferdinando; Mojtahed, Vahid; Hegetschweiler, Karin Tessa; Giupponi, Carlo

2016-06-01

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event.

Uncertainty analysis in vulnerability estimations for elements at risk- a review of concepts and some examples on landslides

NASA Astrophysics Data System (ADS)

Ciurean, R. L.; Glade, T.

2012-04-01

Decision under uncertainty is a constant of everyday life and an important component of risk management and governance. Recently, experts have emphasized the importance of quantifying uncertainty in all phases of landslide risk analysis. Due to its multi-dimensional and dynamic nature, (physical) vulnerability is inherently complex and the "degree of loss" estimates imprecise and to some extent even subjective. Uncertainty analysis introduces quantitative modeling approaches that allow for a more explicitly objective output, improving the risk management process as well as enhancing communication between various stakeholders for better risk governance. This study presents a review of concepts for uncertainty analysis in vulnerability of elements at risk to landslides. Different semi-quantitative and quantitative methods are compared based on their feasibility in real-world situations, hazard dependency, process stage in vulnerability assessment (i.e. input data, model, output), and applicability within an integrated landslide hazard and risk framework. The resulted observations will help to identify current gaps and future needs in vulnerability assessment, including estimation of uncertainty propagation, transferability of the methods, development of visualization tools, but also address basic questions like what is uncertainty and how uncertainty can be quantified or treated in a reliable and reproducible way.

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

A Model for Assessing the Liability of Seemingly Correct Software

NASA Technical Reports Server (NTRS)

Voas, Jeffrey M.; Voas, Larry K.; Miller, Keith W.

1991-01-01

Current research on software reliability does not lend itself to quantitatively assessing the risk posed by a piece of life-critical software. Black-box software reliability models are too general and make too many assumptions to be applied confidently to assessing the risk of life-critical software. We present a model for assessing the risk caused by a piece of software; this model combines software testing results and Hamlet's probable correctness model. We show how this model can assess software risk for those who insure against a loss that can occur if life-critical software fails.

The use of mode of action information in risk assessment: quantitative key events/dose-response framework for modeling the dose-response for key events.

PubMed

Simon, Ted W; Simons, S Stoney; Preston, R Julian; Boobis, Alan R; Cohen, Samuel M; Doerrer, Nancy G; Fenner-Crisp, Penelope A; McMullin, Tami S; McQueen, Charlene A; Rowlands, J Craig

2014-08-01

The HESI RISK21 project formed the Dose-Response/Mode-of-Action Subteam to develop strategies for using all available data (in vitro, in vivo, and in silico) to advance the next-generation of chemical risk assessments. A goal of the Subteam is to enhance the existing Mode of Action/Human Relevance Framework and Key Events/Dose Response Framework (KEDRF) to make the best use of quantitative dose-response and timing information for Key Events (KEs). The resulting Quantitative Key Events/Dose-Response Framework (Q-KEDRF) provides a structured quantitative approach for systematic examination of the dose-response and timing of KEs resulting from a dose of a bioactive agent that causes a potential adverse outcome. Two concepts are described as aids to increasing the understanding of mode of action-Associative Events and Modulating Factors. These concepts are illustrated in two case studies; 1) cholinesterase inhibition by the pesticide chlorpyrifos, which illustrates the necessity of considering quantitative dose-response information when assessing the effect of a Modulating Factor, that is, enzyme polymorphisms in humans, and 2) estrogen-induced uterotrophic responses in rodents, which demonstrate how quantitative dose-response modeling for KE, the understanding of temporal relationships between KEs and a counterfactual examination of hypothesized KEs can determine whether they are Associative Events or true KEs.

Quantitative assessment of changes in landslide risk using a regional scale run-out model

NASA Astrophysics Data System (ADS)

Hussin, Haydar; Chen, Lixia; Ciurean, Roxana; van Westen, Cees; Reichenbach, Paola; Sterlacchini, Simone

2015-04-01

The risk of landslide hazard continuously changes in time and space and is rarely a static or constant phenomena in an affected area. However one of the main challenges of quantitatively assessing changes in landslide risk is the availability of multi-temporal data for the different components of risk. Furthermore, a truly "quantitative" landslide risk analysis requires the modeling of the landslide intensity (e.g. flow depth, velocities or impact pressures) affecting the elements at risk. Such a quantitative approach is often lacking in medium to regional scale studies in the scientific literature or is left out altogether. In this research we modelled the temporal and spatial changes of debris flow risk in a narrow alpine valley in the North Eastern Italian Alps. The debris flow inventory from 1996 to 2011 and multi-temporal digital elevation models (DEMs) were used to assess the susceptibility of debris flow triggering areas and to simulate debris flow run-out using the Flow-R regional scale model. In order to determine debris flow intensities, we used a linear relationship that was found between back calibrated physically based Flo-2D simulations (local scale models of five debris flows from 2003) and the probability values of the Flow-R software. This gave us the possibility to assign flow depth to a total of 10 separate classes on a regional scale. Debris flow vulnerability curves from the literature and one curve specifically for our case study area were used to determine the damage for different material and building types associated with the elements at risk. The building values were obtained from the Italian Revenue Agency (Agenzia delle Entrate) and were classified per cadastral zone according to the Real Estate Observatory data (Osservatorio del Mercato Immobiliare, Agenzia Entrate - OMI). The minimum and maximum market value for each building was obtained by multiplying the corresponding land-use value (€/msq) with building area and number of floors. The risk was calculated by multiplying the vulnerability with the spatial probability and the building values. Changes in landslide risk was assessed using the loss estimation of four different periods: (1) pre-August 2003 disaster, (2) the August 2003 event, (3) post-August 2003 to 2011 and (4) smaller frequent events occurring between the entire 1996-2011 period. One of the major findings of our work was the calculation of a significant decrease in landslide risk after the 2003 disaster compared to the pre-disaster risk period. This indicates the importance of estimating risk after a few years of a major event in order to avoid overestimation or exaggeration of future losses.

Integrating expert opinion with modelling for quantitative multi-hazard risk assessment in the Eastern Italian Alps

NASA Astrophysics Data System (ADS)

Chen, Lixia; van Westen, Cees J.; Hussin, Haydar; Ciurean, Roxana L.; Turkington, Thea; Chavarro-Rincon, Diana; Shrestha, Dhruba P.

2016-11-01

Extreme rainfall events are the main triggering causes for hydro-meteorological hazards in mountainous areas, where development is often constrained by the limited space suitable for construction. In these areas, hazard and risk assessments are fundamental for risk mitigation, especially for preventive planning, risk communication and emergency preparedness. Multi-hazard risk assessment in mountainous areas at local and regional scales remain a major challenge because of lack of data related to past events and causal factors, and the interactions between different types of hazards. The lack of data leads to a high level of uncertainty in the application of quantitative methods for hazard and risk assessment. Therefore, a systematic approach is required to combine these quantitative methods with expert-based assumptions and decisions. In this study, a quantitative multi-hazard risk assessment was carried out in the Fella River valley, prone to debris flows and flood in the north-eastern Italian Alps. The main steps include data collection and development of inventory maps, definition of hazard scenarios, hazard assessment in terms of temporal and spatial probability calculation and intensity modelling, elements-at-risk mapping, estimation of asset values and the number of people, physical vulnerability assessment, the generation of risk curves and annual risk calculation. To compare the risk for each type of hazard, risk curves were generated for debris flows, river floods and flash floods. Uncertainties were expressed as minimum, average and maximum values of temporal and spatial probability, replacement costs of assets, population numbers, and physical vulnerability. These result in minimum, average and maximum risk curves. To validate this approach, a back analysis was conducted using the extreme hydro-meteorological event that occurred in August 2003 in the Fella River valley. The results show a good performance when compared to the historical damage reports.

Noninvasive Biomonitoring Approaches to Determine Dosimetry and Risk Following Acute Chemical Exposure: Analysis of Lead or Organophosphate Insecticide in Saliva

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

Timchalk, Chuck; Poet, Torka S.; Kousba, Ahmed A.

2004-04-01

There is a need to develop approaches for assessing risk associated with acute exposures to a broad-range of chemical agents and to rapidly determine the potential implications to human health. Non-invasive biomonitoring approaches are being developed using reliable portable analytical systems to quantitate dosimetry utilizing readily obtainable body fluids, such as saliva. Saliva has been used to evaluate a broad range of biomarkers, drugs, and environmental contaminants including heavy metals and pesticides. To advance the application of non-invasive biomonitoring a microfluidic/ electrochemical device has also been developed for the analysis of lead (Pb), using square wave anodic stripping voltammetry. Themore » system demonstrates a linear response over a broad concentration range (1 2000 ppb) and is capable of quantitating saliva Pb in rats orally administered acute doses of Pb-acetate. Appropriate pharmacokinetic analyses have been used to quantitate systemic dosimetry based on determination of saliva Pb concentrations. In addition, saliva has recently been used to quantitate dosimetry following exposure to the organophosphate insecticide chlorpyrifos in a rodent model system by measuring the major metabolite, trichloropyridinol, and saliva cholinesterase inhibition following acute exposures. These results suggest that technology developed for non-invasive biomonitoring can provide a sensitive, and portable analytical tool capable of assessing exposure and risk in real-time. By coupling these non-invasive technologies with pharmacokinetic modeling it is feasible to rapidly quantitate acute exposure to a broad range of chemical agents. In summary, it is envisioned that once fully developed, these monitoring and modeling approaches will be useful for accessing acute exposure and health risk.« less

Modeling logistic performance in quantitative microbial risk assessment.

PubMed

Rijgersberg, Hajo; Tromp, Seth; Jacxsens, Liesbeth; Uyttendaele, Mieke

2010-01-01

In quantitative microbial risk assessment (QMRA), food safety in the food chain is modeled and simulated. In general, prevalences, concentrations, and numbers of microorganisms in media are investigated in the different steps from farm to fork. The underlying rates and conditions (such as storage times, temperatures, gas conditions, and their distributions) are determined. However, the logistic chain with its queues (storages, shelves) and mechanisms for ordering products is usually not taken into account. As a consequence, storage times-mutually dependent in successive steps in the chain-cannot be described adequately. This may have a great impact on the tails of risk distributions. Because food safety risks are generally very small, it is crucial to model the tails of (underlying) distributions as accurately as possible. Logistic performance can be modeled by describing the underlying planning and scheduling mechanisms in discrete-event modeling. This is common practice in operations research, specifically in supply chain management. In this article, we present the application of discrete-event modeling in the context of a QMRA for Listeria monocytogenes in fresh-cut iceberg lettuce. We show the potential value of discrete-event modeling in QMRA by calculating logistic interventions (modifications in the logistic chain) and determining their significance with respect to food safety.

Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial.

PubMed

Bartlett, John M S; Christiansen, Jason; Gustavson, Mark; Rimm, David L; Piper, Tammy; van de Velde, Cornelis J H; Hasenburg, Annette; Kieback, Dirk G; Putter, Hein; Markopoulos, Christos J; Dirix, Luc Y; Seynaeve, Caroline; Rea, Daniel W

2016-01-01

Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy. To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies. The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3'-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling. The QIF model was highly significant for prediction of residual risk (P < .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P < .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model. The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.

Comparison of recreational health risks associated with surfing and swimming in dry weather and post-storm conditions at Southern California beaches using quantitative microbial risk assessment (QMRA).

PubMed

Tseng, Linda Y; Jiang, Sunny C

2012-05-01

Southern California is an increasingly urbanized hotspot for surfing, thus it is of great interest to assess the human illness risks associated with this popular ocean recreational water sport from exposure to fecal bacteria contaminated coastal waters. Quantitative microbial risk assessments were applied to eight popular Southern California beaches using readily available enterococcus and fecal coliform data and dose-response models to compare health risks associated with surfing during dry weather and storm conditions. The results showed that the level of gastrointestinal illness risks from surfing post-storm events was elevated, with the probability of exceeding the US EPA health risk guideline up to 28% of the time. The surfing risk was also elevated in comparison with swimming at the same beach due to ingestion of greater volume of water. The study suggests that refinement of dose-response model, improving monitoring practice and better surfer behavior surveillance will improve the risk estimation. Copyright © 2012 Elsevier Ltd. All rights reserved.

Assessing the risk posed by natural hazards to infrastructures

NASA Astrophysics Data System (ADS)

Eidsvig, Unni; Kristensen, Krister; Vidar Vangelsten, Bjørn

2015-04-01

The modern society is increasingly dependent on infrastructures to maintain its function, and disruption in one of the infrastructure systems may have severe consequences. The Norwegian municipalities have, according to legislation, a duty to carry out a risk and vulnerability analysis and plan and prepare for emergencies in a short- and long term perspective. Vulnerability analysis of the infrastructures and their interdependencies is an important part of this analysis. This paper proposes a model for assessing the risk posed by natural hazards to infrastructures. The model prescribes a three level analysis with increasing level of detail, moving from qualitative to quantitative analysis. This paper focuses on the second level, which consists of a semi-quantitative analysis. The purpose of this analysis is to perform a screening of the scenarios of natural hazards threatening the infrastructures identified in the level 1 analysis and investigate the need for further analyses, i.e. level 3 quantitative analyses. The proposed level 2 analysis considers the frequency of the natural hazard, different aspects of vulnerability including the physical vulnerability of the infrastructure itself and the societal dependency on the infrastructure. An indicator-based approach is applied, ranking the indicators on a relative scale. The proposed indicators characterize the robustness of the infrastructure, the importance of the infrastructure as well as interdependencies between society and infrastructure affecting the potential for cascading effects. Each indicator is ranked on a 1-5 scale based on pre-defined ranking criteria. The aggregated risk estimate is a combination of the semi-quantitative vulnerability indicators, as well as quantitative estimates of the frequency of the natural hazard and the number of users of the infrastructure. Case studies for two Norwegian municipalities are presented, where risk to primary road, water supply and power network threatened by storm and landslide is assessed. The application examples show that the proposed model provides a useful tool for screening of undesirable events, with the ultimate goal to reduce the societal vulnerability.

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

NASA Astrophysics Data System (ADS)

Balbi, S.; Villa, F.; Mojtahed, V.; Hegetschweiler, K. T.; Giupponi, C.

2015-10-01

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of: (1) likelihood of non-fatal physical injury; (2) likelihood of post-traumatic stress disorder; (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the benefits of improving an existing Early Warning System, taking into account the reliability, lead-time and scope (i.e. coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event: about 75 % of fatalities, 25 % of injuries and 18 % of post-traumatic stress disorders could be avoided.

EVALUATING QUANTITATIVE FORMULAS FOR DOSE-RESPONSE ASSESSMENT OF CHEMICAL MIXTURES

EPA Science Inventory

Risk assessment formulas are often distinguished from dose-response models by being rough but necessary. The evaluation of these rough formulas is described here, using the example of mixture risk assessment. Two conditions make the dose-response part of mixture risk assessment d...

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.

The Functional Resonance Analysis Method for a systemic risk based environmental auditing in a sinter plant: A semi-quantitative approach

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

Patriarca, Riccardo, E-mail: riccardo.patriarca@uniroma1.it; Di Gravio, Giulio; Costantino, Francesco

Environmental auditing is a main issue for any production plant and assessing environmental performance is crucial to identify risks factors. The complexity of current plants arises from interactions among technological, human and organizational system components, which are often transient and not easily detectable. The auditing thus requires a systemic perspective, rather than focusing on individual behaviors, as emerged in recent research in the safety domain for socio-technical systems. We explore the significance of modeling the interactions of system components in everyday work, by the application of a recent systemic method, i.e. the Functional Resonance Analysis Method (FRAM), in order tomore » define dynamically the system structure. We present also an innovative evolution of traditional FRAM following a semi-quantitative approach based on Monte Carlo simulation. This paper represents the first contribution related to the application of FRAM in the environmental context, moreover considering a consistent evolution based on Monte Carlo simulation. The case study of an environmental risk auditing in a sinter plant validates the research, showing the benefits in terms of identifying potential critical activities, related mitigating actions and comprehensive environmental monitoring indicators. - Highlights: • We discuss the relevance of a systemic risk based environmental audit. • We present FRAM to represent functional interactions of the system. • We develop a semi-quantitative FRAM framework to assess environmental risks. • We apply the semi-quantitative FRAM framework to build a model for a sinter plant.« less

PBPK Models, BBDR Models, and Virtual Tissues: How Will They Contribute to the Use of Toxicity Pathways in Risk Assessment?

EPA Science Inventory

Accuracy in risk assessment, which is desirable in order to ensure protection of the public health while avoiding over-regulation of economically-important substances, requires quantitatively accurate, in vivo descriptions of dose-response and time-course behaviors. This level of...

An ounce of prevention or a pound of cure: bioeconomic risk analysis of invasive species.

PubMed

Leung, Brian; Lodge, David M; Finnoff, David; Shogren, Jason F; Lewis, Mark A; Lamberti, Gary

2002-12-07

Numbers of non-indigenous species--species introduced from elsewhere - are increasing rapidly worldwide, causing both environmental and economic damage. Rigorous quantitative risk-analysis frameworks, however, for invasive species are lacking. We need to evaluate the risks posed by invasive species and quantify the relative merits of different management strategies (e.g. allocation of resources between prevention and control). We present a quantitative bioeconomic modelling framework to analyse risks from non-indigenous species to economic activity and the environment. The model identifies the optimal allocation of resources to prevention versus control, acceptable invasion risks and consequences of invasion to optimal investments (e.g. labour and capital). We apply the model to zebra mussels (Dreissena polymorpha), and show that society could benefit by spending up to US$324 000 year(-1) to prevent invasions into a single lake with a power plant. By contrast, the US Fish and Wildlife Service spent US$825 000 in 2001 to manage all aquatic invaders in all US lakes. Thus, greater investment in prevention is warranted.

Applying Qualitative Hazard Analysis to Support Quantitative Safety Analysis for Proposed Reduced Wake Separation Conops

NASA Technical Reports Server (NTRS)

Shortle, John F.; Allocco, Michael

2005-01-01

This paper describes a scenario-driven hazard analysis process to identify, eliminate, and control safety-related risks. Within this process, we develop selective criteria to determine the applicability of applying engineering modeling to hypothesized hazard scenarios. This provides a basis for evaluating and prioritizing the scenarios as candidates for further quantitative analysis. We have applied this methodology to proposed concepts of operations for reduced wake separation for closely spaced parallel runways. For arrivals, the process identified 43 core hazard scenarios. Of these, we classified 12 as appropriate for further quantitative modeling, 24 that should be mitigated through controls, recommendations, and / or procedures (that is, scenarios not appropriate for quantitative modeling), and 7 that have the lowest priority for further analysis.

An innovative expression model of human health risk based on the quantitative analysis of soil metals sources contribution in different spatial scales.

PubMed

Zhang, Yimei; Li, Shuai; Wang, Fei; Chen, Zhuang; Chen, Jie; Wang, Liqun

2018-09-01

Toxicity of heavy metals from industrialization poses critical concern, and analysis of sources associated with potential human health risks is of unique significance. Assessing human health risk of pollution sources (factored health risk) concurrently in the whole and the sub region can provide more instructive information to protect specific potential victims. In this research, we establish a new expression model of human health risk based on quantitative analysis of sources contribution in different spatial scales. The larger scale grids and their spatial codes are used to initially identify the level of pollution risk, the type of pollution source and the sensitive population at high risk. The smaller scale grids and their spatial codes are used to identify the contribution of various sources of pollution to each sub region (larger grid) and to assess the health risks posed by each source for each sub region. The results of case study show that, for children (sensitive populations, taking school and residential area as major region of activity), the major pollution source is from the abandoned lead-acid battery plant (ALP), traffic emission and agricultural activity. The new models and results of this research present effective spatial information and useful model for quantifying the hazards of source categories and human health a t complex industrial system in the future. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quantitative Prediction of Systemic Toxicity Points of Departure (OpenTox USA 2017)

EPA Science Inventory

Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative models based on chemical structure information, are c...

A probabilistic method for computing quantitative risk indexes from medical injuries compensation claims.

PubMed

Dalle Carbonare, S; Folli, F; Patrini, E; Giudici, P; Bellazzi, R

2013-01-01

The increasing demand of health care services and the complexity of health care delivery require Health Care Organizations (HCOs) to approach clinical risk management through proper methods and tools. An important aspect of risk management is to exploit the analysis of medical injuries compensation claims in order to reduce adverse events and, at the same time, to optimize the costs of health insurance policies. This work provides a probabilistic method to estimate the risk level of a HCO by computing quantitative risk indexes from medical injury compensation claims. Our method is based on the estimate of a loss probability distribution from compensation claims data through parametric and non-parametric modeling and Monte Carlo simulations. The loss distribution can be estimated both on the whole dataset and, thanks to the application of a Bayesian hierarchical model, on stratified data. The approach allows to quantitatively assessing the risk structure of the HCO by analyzing the loss distribution and deriving its expected value and percentiles. We applied the proposed method to 206 cases of injuries with compensation requests collected from 1999 to the first semester of 2007 by the HCO of Lodi, in the Northern part of Italy. We computed the risk indexes taking into account the different clinical departments and the different hospitals involved. The approach proved to be useful to understand the HCO risk structure in terms of frequency, severity, expected and unexpected loss related to adverse events.

A quantitative assessment of risks of heavy metal residues in laundered shop towels and their use by workers.

PubMed

Connor, Kevin; Magee, Brian

2014-10-01

This paper presents a risk assessment of exposure to metal residues in laundered shop towels by workers. The concentrations of 27 metals measured in a synthetic sweat leachate were used to estimate the releasable quantity of metals which could be transferred to workers' skin. Worker exposure was evaluated quantitatively with an exposure model that focused on towel-to-hand transfer and subsequent hand-to-food or -mouth transfers. The exposure model was based on conservative, but reasonable assumptions regarding towel use and default exposure factor values from the published literature or regulatory guidance. Transfer coefficients were derived from studies representative of the exposures to towel users. Contact frequencies were based on assumed high-end use of shop towels, but constrained by a theoretical maximum dermal loading. The risk estimates for workers developed for all metals were below applicable regulatory risk benchmarks. The risk assessment for lead utilized the Adult Lead Model and concluded that predicted lead intakes do not constitute a significant health hazard based on potential worker exposures. Uncertainties are discussed in relation to the overall confidence in the exposure estimates developed for each exposure pathway and the likelihood that the exposure model is under- or overestimating worker exposures and risk. Copyright © 2014 Elsevier Inc. All rights reserved.

Multiple-Strain Approach and Probabilistic Modeling of Consumer Habits in Quantitative Microbial Risk Assessment: A Quantitative Assessment of Exposure to Staphylococcal Enterotoxin A in Raw Milk.

PubMed

Crotta, Matteo; Rizzi, Rita; Varisco, Giorgio; Daminelli, Paolo; Cunico, Elena Cosciani; Luini, Mario; Graber, Hans Ulrich; Paterlini, Franco; Guitian, Javier

2016-03-01

Quantitative microbial risk assessment (QMRA) models are extensively applied to inform management of a broad range of food safety risks. Inevitably, QMRA modeling involves an element of simplification of the biological process of interest. Two features that are frequently simplified or disregarded are the pathogenicity of multiple strains of a single pathogen and consumer behavior at the household level. In this study, we developed a QMRA model with a multiple-strain approach and a consumer phase module (CPM) based on uncertainty distributions fitted from field data. We modeled exposure to staphylococcal enterotoxin A in raw milk in Lombardy; a specific enterotoxin production module was thus included. The model is adaptable and could be used to assess the risk related to other pathogens in raw milk as well as other staphylococcal enterotoxins. The multiplestrain approach, implemented as a multinomial process, allowed the inclusion of variability and uncertainty with regard to pathogenicity at the bacterial level. Data from 301 questionnaires submitted to raw milk consumers were used to obtain uncertainty distributions for the CPM. The distributions were modeled to be easily updatable with further data or evidence. The sources of uncertainty due to the multiple-strain approach and the CPM were identified, and their impact on the output was assessed by comparing specific scenarios to the baseline. When the distributions reflecting the uncertainty in consumer behavior were fixed to the 95th percentile, the risk of exposure increased up to 160 times. This reflects the importance of taking into consideration the diversity of consumers' habits at the household level and the impact that the lack of knowledge about variables in the CPM can have on the final QMRA estimates. The multiple-strain approach lends itself to use in other food matrices besides raw milk and allows the model to better capture the complexity of the real world and to be capable of geographical specificity.

Using Quantitative Structure-Activity Relationship Modeling to Quantitatively Predict the Developmental Toxicity of Halogenated Azole compounds

EPA Science Inventory

Developmental toxicity is a relevant endpoint for the comprehensive assessment of human health risk from chemical exposure. However, animal developmental toxicity studies remain unavailable for many environmental contaminants due to the complexity and cost of these types of analy...

An Overview of Quantitative Risk Assessment of Space Shuttle Propulsion Elements

NASA Technical Reports Server (NTRS)

Safie, Fayssal M.

1998-01-01

Since the Space Shuttle Challenger accident in 1986, NASA has been working to incorporate quantitative risk assessment (QRA) in decisions concerning the Space Shuttle and other NASA projects. One current major NASA QRA study is the creation of a risk model for the overall Space Shuttle system. The model is intended to provide a tool to estimate Space Shuttle risk and to perform sensitivity analyses/trade studies, including the evaluation of upgrades. Marshall Space Flight Center (MSFC) is a part of the NASA team conducting the QRA study; MSFC responsibility involves modeling the propulsion elements of the Space Shuttle, namely: the External Tank (ET), the Solid Rocket Booster (SRB), the Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME). This paper discusses the approach that MSFC has used to model its Space Shuttle elements, including insights obtained from this experience in modeling large scale, highly complex systems with a varying availability of success/failure data. Insights, which are applicable to any QRA study, pertain to organizing the modeling effort, obtaining customer buy-in, preparing documentation, and using varied modeling methods and data sources. Also provided is an overall evaluation of the study results, including the strengths and the limitations of the MSFC QRA approach and of qRA technology in general.

Dose-Response Association Between Physical Activity and Incident Hypertension: A Systematic Review and Meta-Analysis of Cohort Studies.

PubMed

Liu, Xuejiao; Zhang, Dongdong; Liu, Yu; Sun, Xizhuo; Han, Chengyi; Wang, Bingyuan; Ren, Yongcheng; Zhou, Junmei; Zhao, Yang; Shi, Yuanyuan; Hu, Dongsheng; Zhang, Ming

2017-05-01

Despite the inverse association between physical activity (PA) and incident hypertension, a comprehensive assessment of the quantitative dose-response association between PA and hypertension has not been reported. We performed a meta-analysis, including dose-response analysis, to quantitatively evaluate this association. We searched PubMed and Embase databases for articles published up to November 1, 2016. Random effects generalized least squares regression models were used to assess the quantitative association between PA and hypertension risk across studies. Restricted cubic splines were used to model the dose-response association. We identified 22 articles (29 studies) investigating the risk of hypertension with leisure-time PA or total PA, including 330 222 individuals and 67 698 incident cases of hypertension. The risk of hypertension was reduced by 6% (relative risk, 0.94; 95% confidence interval, 0.92-0.96) with each 10 metabolic equivalent of task h/wk increment of leisure-time PA. We found no evidence of a nonlinear dose-response association of PA and hypertension ( P nonlinearity =0.094 for leisure-time PA and 0.771 for total PA). With the linear cubic spline model, when compared with inactive individuals, for those who met the guidelines recommended minimum level of moderate PA (10 metabolic equivalent of task h/wk), the risk of hypertension was reduced by 6% (relative risk, 0.94; 95% confidence interval, 0.92-0.97). This meta-analysis suggests that additional benefits for hypertension prevention occur as the amount of PA increases. © 2017 American Heart Association, Inc.

Advanced risk assessment of the effects of graphite fibers on electronic and electric equipment, phase 1. [simulating vulnerability to airports and communities from fibers released during aircraft fires

NASA Technical Reports Server (NTRS)

Pocinki, L. S.; Kaplan, L. D.; Cornell, M. E.; Greenstone, R.

1979-01-01

A model was developed to generate quantitative estimates of the risk associated with the release of graphite fibers during fires involving commercial aircraft constructed with graphite fiber composite materials. The model was used to estimate the risk associated with accidents at several U.S. airports. These results were then combined to provide an estimate of the total risk to the nation.

Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.

PubMed

Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H; Bisanzio, Donal; Liu, Yang; Remais, Justin V

2014-01-01

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.

Pathway models for analysing and managing the introduction of alien plant pests—an overview and categorization

Treesearch

J.C. Douma; M. Pautasso; R.C. Venette; C. Robinet; L. Hemerik; M.C.M. Mourits; J. Schans; W. van der Werf

2016-01-01

Alien plant pests are introduced into new areas at unprecedented rates through global trade, transport, tourism and travel, threatening biodiversity and agriculture. Increasingly, the movement and introduction of pests is analysed with pathway models to provide risk managers with quantitative estimates of introduction risks and effectiveness of management options....

Agency Problems and Airport Security: Quantitative and Qualitative Evidence on the Impact of Security Training.

PubMed

de Gramatica, Martina; Massacci, Fabio; Shim, Woohyun; Turhan, Uğur; Williams, Julian

2017-02-01

We analyze the issue of agency costs in aviation security by combining results from a quantitative economic model with a qualitative study based on semi-structured interviews. Our model extends previous principal-agent models by combining the traditional fixed and varying monetary responses to physical and cognitive effort with nonmonetary welfare and potentially transferable value of employees' own human capital. To provide empirical evidence for the tradeoffs identified in the quantitative model, we have undertaken an extensive interview process with regulators, airport managers, security personnel, and those tasked with training security personnel from an airport operating in a relatively high-risk state, Turkey. Our results indicate that the effectiveness of additional training depends on the mix of "transferable skills" and "emotional" buy-in of the security agents. Principals need to identify on which side of a critical tipping point their agents are to ensure that additional training, with attached expectations of the burden of work, aligns the incentives of employees with the principals' own objectives. © 2016 Society for Risk Analysis.

Quantitative assessment of risk reduction from hand washing with antibacterial soaps.

PubMed

Gibson, L L; Rose, J B; Haas, C N; Gerba, C P; Rusin, P A

2002-01-01

The Centers for Disease Control and Prevention have estimated that there are 3,713,000 cases of infectious disease associated with day care facilities each year. The objective of this study was to examine the risk reduction achieved from using different soap formulations after diaper changing using a microbial quantitative risk assessment approach. To achieve this, a probability of infection model and an exposure assessment based on micro-organism transfer were used to evaluate the efficacy of different soap formulations in reducing the probability of disease following hand contact with an enteric pathogen. Based on this model, it was determined that the probability of infection ranged from 24/100 to 91/100 for those changing diapers of babies with symptomatic shigellosis who used a control product (soap without an antibacterial ingredient), 22/100 to 91/100 for those who used an antibacterial soap (chlorohexadine 4%), and 15/100 to 90/100 for those who used a triclosan (1.5%) antibacterial soap. Those with asymptomatic shigellosis who used a non-antibacterial control soap had a risk between 49/100,000 and 53/100, those who used the 4% chlorohexadine-containing soap had a risk between 43/100,000 and 51/100, and for those who used a 1.5% triclosan soap had a risk between 21/100,000 and 43/100. The adequate washing of hands after diapering reduces risk and can be further reduced by a factor of 20% by the use of an antibacterial soap. Quantitative risk assessment is a valuable tool in the evaluation of household sanitizing agents and low risk outcomes.

Cultural consensus modeling to measure transactional sex in Swaziland: Scale building and validation.

PubMed

Fielding-Miller, Rebecca; Dunkle, Kristin L; Cooper, Hannah L F; Windle, Michael; Hadley, Craig

2016-01-01

Transactional sex is associated with increased risk of HIV and gender based violence in southern Africa and around the world. However the typical quantitative operationalization, "the exchange of gifts or money for sex," can be at odds with a wide array of relationship types and motivations described in qualitative explorations. To build on the strengths of both qualitative and quantitative research streams, we used cultural consensus models to identify distinct models of transactional sex in Swaziland. The process allowed us to build and validate emic scales of transactional sex, while identifying key informants for qualitative interviews within each model to contextualize women's experiences and risk perceptions. We used logistic and multinomial logistic regression models to measure associations with condom use and social status outcomes. Fieldwork was conducted between November 2013 and December 2014 in the Hhohho and Manzini regions. We identified three distinct models of transactional sex in Swaziland based on 124 Swazi women's emic valuation of what they hoped to receive in exchange for sex with their partners. In a clinic-based survey (n = 406), consensus model scales were more sensitive to condom use than the etic definition. Model consonance had distinct effects on social status for the three different models. Transactional sex is better measured as an emic spectrum of expectations within a relationship, rather than an etic binary relationship type. Cultural consensus models allowed us to blend qualitative and quantitative approaches to create an emicly valid quantitative scale grounded in qualitative context. Copyright © 2015 Elsevier Ltd. All rights reserved.

Potential impacts of radon, terrestrial gamma and cosmic rays on childhood leukemia in France: a quantitative risk assessment.

PubMed

Laurent, Olivier; Ancelet, Sophie; Richardson, David B; Hémon, Denis; Ielsch, Géraldine; Demoury, Claire; Clavel, Jacqueline; Laurier, Dominique

2013-05-01

Previous epidemiological studies and quantitative risk assessments (QRA) have suggested that natural background radiation may be a cause of childhood leukemia. The present work uses a QRA approach to predict the excess risk of childhood leukemia in France related to three components of natural radiation: radon, cosmic rays and terrestrial gamma rays, using excess relative and absolute risk models proposed by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Both models were developed from the Life Span Study (LSS) of Japanese A-bomb survivors. Previous risk assessments were extended by considering uncertainties in radiation-related leukemia risk model parameters as part of this process, within a Bayesian framework. Estimated red bone marrow doses cumulated during childhood by the average French child due to radon, terrestrial gamma and cosmic rays are 4.4, 7.5 and 4.3 mSv, respectively. The excess fractions of cases (expressed as percentages) associated with these sources of natural radiation are 20 % [95 % credible interval (CI) 0-68 %] and 4 % (95 % CI 0-11 %) under the excess relative and excess absolute risk models, respectively. The large CIs, as well as the different point estimates obtained under these two models, highlight the uncertainties in predictions of radiation-related childhood leukemia risks. These results are only valid provided that models developed from the LSS can be transferred to the population of French children and to chronic natural radiation exposures, and must be considered in view of the currently limited knowledge concerning other potential risk factors for childhood leukemia. Last, they emphasize the need for further epidemiological investigations of the effects of natural radiation on childhood leukemia to reduce uncertainties and help refine radiation protection standards.

A quantitative benefit-risk assessment approach to improve decision making in drug development: Application of a multicriteria decision analysis model in the development of combination therapy for overactive bladder.

PubMed

de Greef-van der Sandt, I; Newgreen, D; Schaddelee, M; Dorrepaal, C; Martina, R; Ridder, A; van Maanen, R

2016-04-01

A multicriteria decision analysis (MCDA) approach was developed and used to estimate the benefit-risk of solifenacin and mirabegron and their combination in the treatment of overactive bladder (OAB). The objectives were 1) to develop an MCDA tool to compare drug effects in OAB quantitatively, 2) to establish transparency in the evaluation of the benefit-risk profile of various dose combinations, and 3) to quantify the added value of combination use compared to monotherapies. The MCDA model was developed using efficacy, safety, and tolerability attributes and the results of a phase II factorial design combination study were evaluated. Combinations of solifenacin 5 mg and mirabegron 25 mg and mirabegron 50 (5+25 and 5+50) scored the highest clinical utility and supported combination therapy development of solifenacin and mirabegron for phase III clinical development at these dose regimens. This case study underlines the benefit of using a quantitative approach in clinical drug development programs. © 2015 The American Society for Clinical Pharmacology and Therapeutics.

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 Pittsburgh Cervical Cancer Screening Model: a risk assessment tool.

PubMed

Austin, R Marshall; Onisko, Agnieszka; Druzdzel, Marek J

2010-05-01

Evaluation of cervical cancer screening has grown increasingly complex with the introduction of human papillomavirus (HPV) vaccination and newer screening technologies approved by the US Food and Drug Administration. To create a unique Pittsburgh Cervical Cancer Screening Model (PCCSM) that quantifies risk for histopathologic cervical precancer (cervical intraepithelial neoplasia [CIN] 2, CIN3, and adenocarcinoma in situ) and cervical cancer in an environment predominantly using newer screening technologies. The PCCSM is a dynamic Bayesian network consisting of 19 variables available in the laboratory information system, including patient history data (most recent HPV vaccination data), Papanicolaou test results, high-risk HPV results, procedure data, and histopathologic results. The model's graphic structure was based on the published literature. Results from 375 441 patient records from 2005 through 2008 were used to build and train the model. Additional data from 45 930 patients were used to test the model. The PCCSM compares risk quantitatively over time for histopathologically verifiable CIN2, CIN3, adenocarcinoma in situ, and cervical cancer in screened patients for each current cytology result category and for each HPV result. For each current cytology result, HPV test results affect risk; however, the degree of cytologic abnormality remains the largest positive predictor of risk. Prior history also alters the CIN2, CIN3, adenocarcinoma in situ, and cervical cancer risk for patients with common current cytology and HPV test results. The PCCSM can also generate negative risk projections, estimating the likelihood of the absence of histopathologic CIN2, CIN3, adenocarcinoma in situ, and cervical cancer in screened patients. The PCCSM is a dynamic Bayesian network that computes quantitative cervical disease risk estimates for patients undergoing cervical screening. Continuously updatable with current system data, the PCCSM provides a new tool to monitor cervical disease risk in the evolving postvaccination era.

Comparing listeriosis risks in at-risk populations using a user-friendly quantitative microbial risk assessment tool and epidemiological data.

PubMed

Falk, L E; Fader, K A; Cui, D S; Totton, S C; Fazil, A M; Lammerding, A M; Smith, B A

2016-10-01

Although infection by the pathogenic bacterium Listeria monocytogenes is relatively rare, consequences can be severe, with a high case-fatality rate in vulnerable populations. A quantitative, probabilistic risk assessment tool was developed to compare estimates of the number of invasive listeriosis cases in vulnerable Canadian subpopulations given consumption of contaminated ready-to-eat delicatessen meats and hot dogs, under various user-defined scenarios. The model incorporates variability and uncertainty through Monte Carlo simulation. Processes considered within the model include cross-contamination, growth, risk factor prevalence, subpopulation susceptibilities, and thermal inactivation. Hypothetical contamination events were simulated. Results demonstrated varying risk depending on the consumer risk factors and implicated product (turkey delicatessen meat without growth inhibitors ranked highest for this scenario). The majority (80%) of listeriosis cases were predicted in at-risk subpopulations comprising only 20% of the total Canadian population, with the greatest number of predicted cases in the subpopulation with dialysis and/or liver disease. This tool can be used to simulate conditions and outcomes under different scenarios, such as a contamination event and/or outbreak, to inform public health interventions.

The role of models in estimating consequences as part of the risk assessment process.

PubMed

Forde-Folle, K; Mitchell, D; Zepeda, C

2011-08-01

The degree of disease risk represented by the introduction, spread, or establishment of one or several diseases through the importation of animals and animal products is assessed by importing countries through an analysis of risk. The components of a risk analysis include hazard identification, risk assessment, risk management, and risk communication. A risk assessment starts with identification of the hazard(s) and then continues with four interrelated steps: release assessment, exposure assessment, consequence assessment, and risk estimation. Risk assessments may be either qualitative or quantitative. This paper describes how, through the integration of epidemiological and economic models, the potential adverse biological and economic consequences of exposure can be quantified.

Probabilistic framework for product design optimization and risk management

NASA Astrophysics Data System (ADS)

Keski-Rahkonen, J. K.

2018-05-01

Probabilistic methods have gradually gained ground within engineering practices but currently it is still the industry standard to use deterministic safety margin approaches to dimensioning components and qualitative methods to manage product risks. These methods are suitable for baseline design work but quantitative risk management and product reliability optimization require more advanced predictive approaches. Ample research has been published on how to predict failure probabilities for mechanical components and furthermore to optimize reliability through life cycle cost analysis. This paper reviews the literature for existing methods and tries to harness their best features and simplify the process to be applicable in practical engineering work. Recommended process applies Monte Carlo method on top of load-resistance models to estimate failure probabilities. Furthermore, it adds on existing literature by introducing a practical framework to use probabilistic models in quantitative risk management and product life cycle costs optimization. The main focus is on mechanical failure modes due to the well-developed methods used to predict these types of failures. However, the same framework can be applied on any type of failure mode as long as predictive models can be developed.

Assessing the risk posed by natural hazards to infrastructures

NASA Astrophysics Data System (ADS)

Eidsvig, Unni Marie K.; Kristensen, Krister; Vidar Vangelsten, Bjørn

2017-03-01

This paper proposes a model for assessing the risk posed by natural hazards to infrastructures, with a focus on the indirect losses and loss of stability for the population relying on the infrastructure. The model prescribes a three-level analysis with increasing level of detail, moving from qualitative to quantitative analysis. The focus is on a methodology for semi-quantitative analyses to be performed at the second level. The purpose of this type of analysis is to perform a screening of the scenarios of natural hazards threatening the infrastructures, identifying the most critical scenarios and investigating the need for further analyses (third level). The proposed semi-quantitative methodology considers the frequency of the natural hazard, different aspects of vulnerability, including the physical vulnerability of the infrastructure itself, and the societal dependency on the infrastructure. An indicator-based approach is applied, ranking the indicators on a relative scale according to pre-defined ranking criteria. The proposed indicators, which characterise conditions that influence the probability of an infrastructure malfunctioning caused by a natural event, are defined as (1) robustness and buffer capacity, (2) level of protection, (3) quality/level of maintenance and renewal, (4) adaptability and quality of operational procedures and (5) transparency/complexity/degree of coupling. Further indicators describe conditions influencing the socio-economic consequences of the infrastructure malfunctioning, such as (1) redundancy and/or substitution, (2) cascading effects and dependencies, (3) preparedness and (4) early warning, emergency response and measures. The aggregated risk estimate is a combination of the semi-quantitative vulnerability indicators, as well as quantitative estimates of the frequency of the natural hazard, the potential duration of the infrastructure malfunctioning (e.g. depending on the required restoration effort) and the number of users of the infrastructure. Case studies for two Norwegian municipalities are presented for demonstration purposes, where risk posed by adverse weather and natural hazards to primary road, water supply and power networks is assessed. The application examples show that the proposed model provides a useful tool for screening of potential undesirable events, contributing to a targeted reduction of the risk.

Statistical Modeling for Radiation Hardness Assurance: Toward Bigger Data

NASA Technical Reports Server (NTRS)

Ladbury, R.; Campola, M. J.

2015-01-01

New approaches to statistical modeling in radiation hardness assurance are discussed. These approaches yield quantitative bounds on flight-part radiation performance even in the absence of conventional data sources. This allows the analyst to bound radiation risk at all stages and for all decisions in the RHA process. It also allows optimization of RHA procedures for the project's risk tolerance.

EPA CENTER FOR EXPOSURE ASSESSMENT MODELING (CEAM)

EPA Science Inventory

The EPA Center for Exposure Assessment Modeling (CEAM) supports the Agency and professional community in environmental, risk-based decision-making by expanding their applications expertise for quantitatively assessing pollutant exposure via aquatic, terrestrial, and multimedia pa...

Quantitative fetal fibronectin testing in combination with cervical length measurement in the prediction of spontaneous preterm delivery in symptomatic women.

PubMed

Bruijn, Mmc; Vis, J Y; Wilms, F F; Oudijk, M A; Kwee, A; Porath, M M; Oei, G; Scheepers, Hcj; Spaanderman, Mea; Bloemenkamp, Kwm; Haak, M C; Bolte, A C; Vandenbussche, Fpha; Woiski, M D; Bax, C J; Cornette, Jmj; Duvekot, J J; Nij Bijvanck, Bwa; van Eyck, J; Franssen, Mtm; Sollie, K M; van der Post, Jam; Bossuyt, Pmm; Opmeer, B C; Kok, M; Mol, Bwj; van Baaren, G-J

2016-11-01

To evaluate whether in symptomatic women, the combination of quantitative fetal fibronectin (fFN) testing and cervical length (CL) improves the prediction of preterm delivery (PTD) within 7 days compared with qualitative fFN and CL. Post hoc analysis of frozen fFN samples of a nationwide cohort study. Ten perinatal centres in the Netherlands. Symptomatic women between 24 and 34 weeks of gestation. The risk of PTD <7 days was estimated in predefined CL and fFN strata. We used logistic regression to develop a model including quantitative fFN and CL, and one including qualitative fFN (threshold 50 ng/ml) and CL. We compared the models' capacity to identify women at low risk (<5%) for delivery within 7 days using a reclassification table. Spontaneous delivery within 7 days after study entry. We studied 350 women, of whom 69 (20%) delivered within 7 days. The risk of PTD in <7 days ranged from 2% in the lowest fFN group (<10 ng/ml) to 71% in the highest group (>500 ng/ml). Multivariable logistic regression showed an increasing risk of PTD in <7 days with rising fFN concentration [10-49 ng/ml: odds ratio (OR) 1.3, 95% confidence interval (95% CI) 0.23-7.0; 50-199 ng/ml: OR 3.2, 95% CI 0.79-13; 200-499 ng/ml: OR 9.0, 95% CI 2.3-35; >500 ng/ml: OR 39, 95% CI 9.4-164] and shortening of the CL (OR 0.86 per mm, 95% CI 0.82-0.90). Use of quantitative fFN instead of qualitative fFN resulted in reclassification of 18 (5%) women from high to low risk, of whom one (6%) woman delivered within 7 days. In symptomatic women, quantitative fFN testing does not improve the prediction of PTD within 7 days compared with qualitative fFN testing in combination with CL measurement in terms of reclassification from high to low (<5%) risk, but it adds value across the risk range. Quantitative fFN testing adds value to qualitative fFN testing with CL measurement in the prediction of PTD. © 2015 Royal College of Obstetricians and Gynaecologists.

People's Risk Recognition Preceding Evacuation and Its Role in Demand Modeling and Planning.

PubMed

Urata, Junji; Pel, Adam J

2018-05-01

Evacuation planning and management involves estimating the travel demand in the event that such action is required. This is usually done as a function of people's decision to evacuate, which we show is strongly linked to their risk awareness. We use an empirical data set, which shows tsunami evacuation behavior, to demonstrate that risk recognition is not synonymous with objective risk, but is instead determined by a combination of factors including risk education, information, and sociodemographics, and that it changes dynamically over time. Based on these findings, we formulate an ordered logit model to describe risk recognition combined with a latent class model to describe evacuation choices. Our proposed evacuation choice model along with a risk recognition class can evaluate quantitatively the influence of disaster mitigation measures, risk education, and risk information. The results obtained from the risk recognition model show that risk information has a greater impact in the sense that people recognize their high risk. The results of the evacuation choice model show that people who are unaware of their risk take a longer time to evacuate. © 2017 Society for Risk Analysis.

Spacecraft Complexity Subfactors and Implications on Future Cost Growth

NASA Technical Reports Server (NTRS)

Leising, Charles J.; Wessen, Randii; Ellyin, Ray; Rosenberg, Leigh; Leising, Adam

2013-01-01

During the last ten years the Jet Propulsion Laboratory has used a set of cost-risk subfactors to independently estimate the magnitude of development risks that may not be covered in the high level cost models employed during early concept development. Within the last several years the Laboratory has also developed a scale of Concept Maturity Levels with associated criteria to quantitatively assess a concept's maturity. This latter effort has been helpful in determining whether a concept is mature enough for accurate costing but it does not provide any quantitative estimate of cost risk. Unfortunately today's missions are significantly more complex than when the original cost-risk subfactors were first formulated. Risks associated with complex missions are not being adequately evaluated and future cost growth is being underestimated. The risk subfactor process needed to be updated.

Quantitative risk stratification in Markov chains with limiting conditional distributions.

PubMed

Chan, David C; Pollett, Philip K; Weinstein, Milton C

2009-01-01

Many clinical decisions require patient risk stratification. The authors introduce the concept of limiting conditional distributions, which describe the equilibrium proportion of surviving patients occupying each disease state in a Markov chain with death. Such distributions can quantitatively describe risk stratification. The authors first establish conditions for the existence of a positive limiting conditional distribution in a general Markov chain and describe a framework for risk stratification using the limiting conditional distribution. They then apply their framework to a clinical example of a treatment indicated for high-risk patients, first to infer the risk of patients selected for treatment in clinical trials and then to predict the outcomes of expanding treatment to other populations of risk. For the general chain, a positive limiting conditional distribution exists only if patients in the earliest state have the lowest combined risk of progression or death. The authors show that in their general framework, outcomes and population risk are interchangeable. For the clinical example, they estimate that previous clinical trials have selected the upper quintile of patient risk for this treatment, but they also show that expanded treatment would weakly dominate this degree of targeted treatment, and universal treatment may be cost-effective. Limiting conditional distributions exist in most Markov models of progressive diseases and are well suited to represent risk stratification quantitatively. This framework can characterize patient risk in clinical trials and predict outcomes for other populations of risk.

Quantitative background parenchymal uptake on molecular breast imaging and breast cancer risk: a case-control study.

PubMed

Hruska, Carrie B; Geske, Jennifer R; Swanson, Tiffinee N; Mammel, Alyssa N; Lake, David S; Manduca, Armando; Conners, Amy Lynn; Whaley, Dana H; Scott, Christopher G; Carter, Rickey E; Rhodes, Deborah J; O'Connor, Michael K; Vachon, Celine M

2018-06-05

Background parenchymal uptake (BPU), which refers to the level of Tc-99m sestamibi uptake within normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor, independent of mammographic density. Prior analyses have used subjective categories to describe BPU. We evaluate a new quantitative method for assessing BPU by testing its reproducibility, comparing quantitative results with previously established subjective BPU categories, and determining the association of quantitative BPU with breast cancer risk. Two nonradiologist operators independently performed region-of-interest analysis on MBI images viewed in conjunction with corresponding digital mammograms. Quantitative BPU was defined as a unitless ratio of the average pixel intensity (counts/pixel) within the fibroglandular tissue versus the average pixel intensity in fat. Operator agreement and the correlation of quantitative BPU measures with subjective BPU categories assessed by expert radiologists were determined. Percent density on mammograms was estimated using Cumulus. The association of quantitative BPU with breast cancer (per one unit BPU) was examined within an established case-control study of 62 incident breast cancer cases and 177 matched controls. Quantitative BPU ranged from 0.4 to 3.2 across all subjects and was on average higher in cases compared to controls (1.4 versus 1.2, p < 0.007 for both operators). Quantitative BPU was strongly correlated with subjective BPU categories (Spearman's r = 0.59 to 0.69, p < 0.0001, for each paired combination of two operators and two radiologists). Interoperator and intraoperator agreement in the quantitative BPU measure, assessed by intraclass correlation, was 0.92 and 0.98, respectively. Quantitative BPU measures showed either no correlation or weak negative correlation with mammographic percent density. In a model adjusted for body mass index and percent density, higher quantitative BPU was associated with increased risk of breast cancer for both operators (OR = 4.0, 95% confidence interval (CI) 1.6-10.1, and 2.4, 95% CI 1.2-4.7). Quantitative measurement of BPU, defined as the ratio of average counts in fibroglandular tissue relative to that in fat, can be reliably performed by nonradiologist operators with a simple region-of-interest analysis tool. Similar to results obtained with subjective BPU categories, quantitative BPU is a functional imaging biomarker of breast cancer risk, independent of mammographic density and hormonal factors.

A Model of Risk Analysis in Analytical Methodology for Biopharmaceutical Quality Control.

PubMed

Andrade, Cleyton Lage; Herrera, Miguel Angel De La O; Lemes, Elezer Monte Blanco

2018-01-01

One key quality control parameter for biopharmaceutical products is the analysis of residual cellular DNA. To determine small amounts of DNA (around 100 pg) that may be in a biologically derived drug substance, an analytical method should be sensitive, robust, reliable, and accurate. In principle, three techniques have the ability to measure residual cellular DNA: radioactive dot-blot, a type of hybridization; threshold analysis; and quantitative polymerase chain reaction. Quality risk management is a systematic process for evaluating, controlling, and reporting of risks that may affects method capabilities and supports a scientific and practical approach to decision making. This paper evaluates, by quality risk management, an alternative approach to assessing the performance risks associated with quality control methods used with biopharmaceuticals, using the tool hazard analysis and critical control points. This tool provides the possibility to find the steps in an analytical procedure with higher impact on method performance. By applying these principles to DNA analysis methods, we conclude that the radioactive dot-blot assay has the largest number of critical control points, followed by quantitative polymerase chain reaction, and threshold analysis. From the analysis of hazards (i.e., points of method failure) and the associated method procedure critical control points, we conclude that the analytical methodology with the lowest risk for performance failure for residual cellular DNA testing is quantitative polymerase chain reaction. LAY ABSTRACT: In order to mitigate the risk of adverse events by residual cellular DNA that is not completely cleared from downstream production processes, regulatory agencies have required the industry to guarantee a very low level of DNA in biologically derived pharmaceutical products. The technique historically used was radioactive blot hybridization. However, the technique is a challenging method to implement in a quality control laboratory: It is laborious, time consuming, semi-quantitative, and requires a radioisotope. Along with dot-blot hybridization, two alternatives techniques were evaluated: threshold analysis and quantitative polymerase chain reaction. Quality risk management tools were applied to compare the techniques, taking into account the uncertainties, the possibility of circumstances or future events, and their effects upon method performance. By illustrating the application of these tools with DNA methods, we provide an example of how they can be used to support a scientific and practical approach to decision making and can assess and manage method performance risk using such tools. This paper discusses, considering the principles of quality risk management, an additional approach to the development and selection of analytical quality control methods using the risk analysis tool hazard analysis and critical control points. This tool provides the possibility to find the method procedural steps with higher impact on method reliability (called critical control points). Our model concluded that the radioactive dot-blot assay has the larger number of critical control points, followed by quantitative polymerase chain reaction and threshold analysis. Quantitative polymerase chain reaction is shown to be the better alternative analytical methodology in residual cellular DNA analysis. © PDA, Inc. 2018.

SPATIAL-TEMPORAL DISTRIBUTION OF WATERBORNE INFECTIOUS DISEASE RISK USING THE HYDRAULIC MODEL AND OUTPATIENT DATA

NASA Astrophysics Data System (ADS)

Amano, Ayako; Sakuma, Taisuke; Kazama, So

This study evaluated waterborne infectious diseases risk and incidence rate around Phonm Penh in Cambodia. We use the hydraulic flood simulation, coliform bacterium diffusion model, dose-response model and outpatient data for quantitative analysis. The results obtained are as follows; 1. The incidence (incidence rate) of diarrhea as water borne diseases risk is 0.14 million people (9%) in the inundation area. 2. The residents in the inundation area are exposed up to 4 times as high risk as daily mean calculated by the integrated model combined in the regional scale. 3.The infectious disease risk due to floods and inundation indicated is effective as an element to explain the risk. The scenario explains 34% number of patient estimated by the outpatient data.

Effects of personalized colorectal cancer risk information on laypersons' interest in colorectal cancer screening: The importance of individual differences.

PubMed

Han, Paul K J; Duarte, Christine W; Daggett, Susannah; Siewers, Andrea; Killam, Bill; Smith, Kahsi A; Freedman, Andrew N

2015-10-01

To evaluate how personalized quantitative colorectal cancer (CRC) risk information affects laypersons' interest in CRC screening, and to explore factors influencing these effects. An online pre-post experiment was conducted in which a convenience sample (N=578) of laypersons, aged >50, were provided quantitative personalized estimates of lifetime CRC risk, calculated by the National Cancer Institute Colorectal Cancer Risk Assessment Tool (CCRAT). Self-reported interest in CRC screening was measured immediately before and after CCRAT use; sociodemographic characteristics and prior CRC screening history were also assessed. Multivariable analyses assessed participants' change in interest in screening, and subgroup differences in this change. Personalized CRC risk information had no overall effect on CRC screening interest, but significant subgroup differences were observed. Change in screening interest was greater among individuals with recent screening (p=.015), higher model-estimated cancer risk (p=.0002), and lower baseline interest (p<.0001), with individuals at highest baseline interest demonstrating negative (not neutral) change in interest. Effects of quantitative personalized CRC risk information on laypersons' interest in CRC screening differ among individuals depending on prior screening history, estimated cancer risk, and baseline screening interest. Personalized cancer risk information has personalized effects-increasing and decreasing screening interest in different individuals. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Space Shuttle Main Engine Quantitative Risk Assessment: Illustrating Modeling of a Complex System with a New QRA Software Package

NASA Technical Reports Server (NTRS)

Smart, Christian

1998-01-01

During 1997, a team from Hernandez Engineering, MSFC, Rocketdyne, Thiokol, Pratt & Whitney, and USBI completed the first phase of a two year Quantitative Risk Assessment (QRA) of the Space Shuttle. The models for the Shuttle systems were entered and analyzed by a new QRA software package. This system, termed the Quantitative Risk Assessment System(QRAS), was designed by NASA and programmed by the University of Maryland. The software is a groundbreaking PC-based risk assessment package that allows the user to model complex systems in a hierarchical fashion. Features of the software include the ability to easily select quantifications of failure modes, draw Event Sequence Diagrams(ESDs) interactively, perform uncertainty and sensitivity analysis, and document the modeling. This paper illustrates both the approach used in modeling and the particular features of the software package. The software is general and can be used in a QRA of any complex engineered system. The author is the project lead for the modeling of the Space Shuttle Main Engines (SSMEs), and this paper focuses on the modeling completed for the SSMEs during 1997. In particular, the groundrules for the study, the databases used, the way in which ESDs were used to model catastrophic failure of the SSMES, the methods used to quantify the failure rates, and how QRAS was used in the modeling effort are discussed. Groundrules were necessary to limit the scope of such a complex study, especially with regard to a liquid rocket engine such as the SSME, which can be shut down after ignition either on the pad or in flight. The SSME was divided into its constituent components and subsystems. These were ranked on the basis of the possibility of being upgraded and risk of catastrophic failure. Once this was done the Shuttle program Hazard Analysis and Failure Modes and Effects Analysis (FMEA) were used to create a list of potential failure modes to be modeled. The groundrules and other criteria were used to screen out the many failure modes that did not contribute significantly to the catastrophic risk. The Hazard Analysis and FMEA for the SSME were also used to build ESDs that show the chain of events leading from the failure mode occurence to one of the following end states: catastrophic failure, engine shutdown, or siccessful operation( successful with respect to the failure mode under consideration).

Mathematical modelling and quantitative methods.

PubMed

Edler, L; Poirier, K; Dourson, M; Kleiner, J; Mileson, B; Nordmann, H; Renwick, A; Slob, W; Walton, K; Würtzen, G

2002-01-01

The present review reports on the mathematical methods and statistical techniques presently available for hazard characterisation. The state of the art of mathematical modelling and quantitative methods used currently for regulatory decision-making in Europe and additional potential methods for risk assessment of chemicals in food and diet are described. Existing practices of JECFA, FDA, EPA, etc., are examined for their similarities and differences. A framework is established for the development of new and improved quantitative methodologies. Areas for refinement, improvement and increase of efficiency of each method are identified in a gap analysis. Based on this critical evaluation, needs for future research are defined. It is concluded from our work that mathematical modelling of the dose-response relationship would improve the risk assessment process. An adequate characterisation of the dose-response relationship by mathematical modelling clearly requires the use of a sufficient number of dose groups to achieve a range of different response levels. This need not necessarily lead to an increase in the total number of animals in the study if an appropriate design is used. Chemical-specific data relating to the mode or mechanism of action and/or the toxicokinetics of the chemical should be used for dose-response characterisation whenever possible. It is concluded that a single method of hazard characterisation would not be suitable for all kinds of risk assessments, and that a range of different approaches is necessary so that the method used is the most appropriate for the data available and for the risk characterisation issue. Future refinements to dose-response characterisation should incorporate more clearly the extent of uncertainty and variability in the resulting output.

Dose response models and a quantitative microbial risk assessment framework for the Mycobacterium avium complex that account for recent developments in molecular biology, taxonomy, and epidemiology.

PubMed

Hamilton, Kerry A; Weir, Mark H; Haas, Charles N

2017-02-01

Mycobacterium avium complex (MAC) is a group of environmentally-transmitted pathogens of great public health importance. This group is known to be harbored, amplified, and selected for more human-virulent characteristics by amoeba species in aquatic biofilms. However, a quantitative microbial risk assessment (QMRA) has not been performed due to the lack of dose response models resulting from significant heterogeneity within even a single species or subspecies of MAC, as well as the range of human susceptibilities to mycobacterial disease. The primary human-relevant species and subspecies responsible for the majority of the human disease burden and present in drinking water, biofilms, and soil are M. avium subsp. hominissuis, M. intracellulare, and M. chimaera. A critical review of the published literature identified important health endpoints, exposure routes, and susceptible populations for MAC risk assessment. In addition, data sets for quantitative dose-response functions were extracted from published in vivo animal dosing experiments. As a result, seven new exponential dose response models for human-relevant species of MAC with endpoints of lung lesions, death, disseminated infection, liver infection, and lymph node lesions are proposed. Although current physical and biochemical tests used in clinical settings do not differentiate between M. avium and M. intracellulare, differentiating between environmental species and subspecies of the MAC can aid in the assessment of health risks and control of MAC sources. A framework is proposed for incorporating the proposed dose response models into susceptible population- and exposure route-specific QMRA models. Copyright © 2016 Elsevier Ltd. All rights reserved.

Occupations at Risk and Organizational Well-Being: An Empirical Test of a Job Insecurity Integrated Model

PubMed Central

Chirumbolo, Antonio; Urbini, Flavio; Callea, Antonino; Lo Presti, Alessandro; Talamo, Alessandra

2017-01-01

One of the more visible effects of the societal changes is the increased feelings of uncertainty in the workforce. In fact, job insecurity represents a crucial occupational risk factor and a major job stressor that has negative consequences on both organizational well-being and individual health. Many studies have focused on the consequences about the fear and the perception of losing the job as a whole (called quantitative job insecurity), while more recently research has begun to examine more extensively the worries and the perceptions of losing valued job features (called qualitative job insecurity). The vast majority of the studies, however, have investigated the effects of quantitative and qualitative job insecurity separately. In this paper, we proposed the Job Insecurity Integrated Model aimed to examine the effects of quantitative job insecurity and qualitative job insecurity on their short-term and long-term outcomes. This model was empirically tested in two independent studies, hypothesizing that qualitative job insecurity mediated the effects of quantitative job insecurity on different outcomes, such as work engagement and organizational identification (Study 1), and job satisfaction, commitment, psychological stress and turnover intention (Study 2). Study 1 was conducted on 329 employees in private firms, while Study 2 on 278 employees in both public sector and private firms. Results robustly showed that qualitative job insecurity totally mediated the effects of quantitative on all the considered outcomes. By showing that the effects of quantitative job insecurity on its outcomes passed through qualitative job insecurity, the Job Insecurity Integrated Model contributes to clarifying previous findings in job insecurity research and puts forward a framework that could profitably produce new investigations with important theoretical and practical implications. PMID:29250013

Occupations at Risk and Organizational Well-Being: An Empirical Test of a Job Insecurity Integrated Model.

PubMed

Chirumbolo, Antonio; Urbini, Flavio; Callea, Antonino; Lo Presti, Alessandro; Talamo, Alessandra

2017-01-01

One of the more visible effects of the societal changes is the increased feelings of uncertainty in the workforce. In fact, job insecurity represents a crucial occupational risk factor and a major job stressor that has negative consequences on both organizational well-being and individual health. Many studies have focused on the consequences about the fear and the perception of losing the job as a whole (called quantitative job insecurity), while more recently research has begun to examine more extensively the worries and the perceptions of losing valued job features (called qualitative job insecurity). The vast majority of the studies, however, have investigated the effects of quantitative and qualitative job insecurity separately. In this paper, we proposed the Job Insecurity Integrated Model aimed to examine the effects of quantitative job insecurity and qualitative job insecurity on their short-term and long-term outcomes. This model was empirically tested in two independent studies, hypothesizing that qualitative job insecurity mediated the effects of quantitative job insecurity on different outcomes, such as work engagement and organizational identification (Study 1), and job satisfaction, commitment, psychological stress and turnover intention (Study 2). Study 1 was conducted on 329 employees in private firms, while Study 2 on 278 employees in both public sector and private firms. Results robustly showed that qualitative job insecurity totally mediated the effects of quantitative on all the considered outcomes. By showing that the effects of quantitative job insecurity on its outcomes passed through qualitative job insecurity, the Job Insecurity Integrated Model contributes to clarifying previous findings in job insecurity research and puts forward a framework that could profitably produce new investigations with important theoretical and practical implications.

Systems Toxicology: From Basic Research to Risk Assessment

PubMed Central

2014-01-01

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment. PMID:24446777

Systems toxicology: from basic research to risk assessment.

PubMed

Sturla, Shana J; Boobis, Alan R; FitzGerald, Rex E; Hoeng, Julia; Kavlock, Robert J; Schirmer, Kristin; Whelan, Maurice; Wilks, Martin F; Peitsch, Manuel C

2014-03-17

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment.

Quantifying Zika: Advancing the Epidemiology of Zika With Quantitative Models.

PubMed

Keegan, Lindsay T; Lessler, Justin; Johansson, Michael A

2017-12-16

When Zika virus (ZIKV) emerged in the Americas, little was known about its biology, pathogenesis, and transmission potential, and the scope of the epidemic was largely hidden, owing to generally mild infections and no established surveillance systems. Surges in congenital defects and Guillain-Barré syndrome alerted the world to the danger of ZIKV. In the context of limited data, quantitative models were critical in reducing uncertainties and guiding the global ZIKV response. Here, we review some of the models used to assess the risk of ZIKV-associated severe outcomes, the potential speed and size of ZIKV epidemics, and the geographic distribution of ZIKV risk. These models provide important insights and highlight significant unresolved questions related to ZIKV and other emerging pathogens. Published by Oxford University Press for the Infectious Diseases Society of America 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.

IWGT report on quantitative approaches to genotoxicity risk ...

EPA Pesticide Factsheets

This is the second of two reports from the International Workshops on Genotoxicity Testing (IWGT) Working Group on Quantitative Approaches to Genetic Toxicology Risk Assessment (the QWG). The first report summarized the discussions and recommendations of the QWG related to the need for quantitative dose–response analysis of genetic toxicology data, the existence and appropriate evaluation of threshold responses, and methods to analyze exposure-response relationships and derive points of departure (PoDs) from which acceptable exposure levels could be determined. This report summarizes the QWG discussions and recommendations regarding appropriate approaches to evaluate exposure-related risks of genotoxic damage, including extrapolation below identified PoDs and across test systems and species. Recommendations include the selection of appropriate genetic endpoints and target tissues, uncertainty factors and extrapolation methods to be considered, the importance and use of information on mode of action, toxicokinetics, metabolism, and exposure biomarkers when using quantitative exposure-response data to determine acceptable exposure levels in human populations or to assess the risk associated with known or anticipated exposures. The empirical relationship between genetic damage (mutation and chromosomal aberration) and cancer in animal models was also examined. It was concluded that there is a general correlation between cancer induction and mutagenic and/or clast

Quantitative Microbial Risk Assessment for Escherichia coli O157:H7 in Fresh-Cut Lettuce.

PubMed

Pang, Hao; Lambertini, Elisabetta; Buchanan, Robert L; Schaffner, Donald W; Pradhan, Abani K

2017-02-01

Leafy green vegetables, including lettuce, are recognized as potential vehicles for foodborne pathogens such as Escherichia coli O157:H7. Fresh-cut lettuce is potentially at high risk of causing foodborne illnesses, as it is generally consumed without cooking. Quantitative microbial risk assessments (QMRAs) are gaining more attention as an effective tool to assess and control potential risks associated with foodborne pathogens. This study developed a QMRA model for E. coli O157:H7 in fresh-cut lettuce and evaluated the effects of different potential intervention strategies on the reduction of public health risks. The fresh-cut lettuce production and supply chain was modeled from field production, with both irrigation water and soil as initial contamination sources, to consumption at home. The baseline model (with no interventions) predicted a mean probability of 1 illness per 10 million servings and a mean of 2,160 illness cases per year in the United States. All intervention strategies evaluated (chlorine, ultrasound and organic acid, irradiation, bacteriophage, and consumer washing) significantly reduced the estimated mean number of illness cases when compared with the baseline model prediction (from 11.4- to 17.9-fold reduction). Sensitivity analyses indicated that retail and home storage temperature were the most important factors affecting the predicted number of illness cases. The developed QMRA model provided a framework for estimating risk associated with consumption of E. coli O157:H7-contaminated fresh-cut lettuce and can guide the evaluation and development of intervention strategies aimed at reducing such risk.

DOSE-RESPONSE ASSESSMENT FOR DEVELOPMENTAL TOXICITY III. STATISTICAL MODELS

EPA Science Inventory

Although quantitative modeling has been central to cancer risk assessment for years, the concept of do@e-response modeling for developmental effects is relatively new. he benchmark dose (BMD) approach has been proposed for use with developmental (as well as other noncancer) endpo...

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...

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

Dourson, M.L.

The quantitative procedures associated with noncancer risk assessment include reference dose (RfD), benchmark dose, and severity modeling. The RfD, which is part of the EPA risk assessment guidelines, is an estimation of a level that is likely to be without any health risk to sensitive individuals. The RfD requires two major judgments: the first is choice of a critical effect(s) and its No Observed Adverse Effect Level (NOAEL); the second judgment is choice of an uncertainty factor. This paper discusses major assumptions and limitations of the RfD model.

Quantitative Assessment the Relationship between p21 rs1059234 Polymorphism and Cancer Risk.

PubMed

Huang, Yong-Sheng; Fan, Qian-Qian; Li, Chuang; Nie, Meng; Quan, Hong-Yang; Wang, Lin

2015-01-01

p21 is a cyclin-dependent kinase inhibitor, which can arrest cell proliferation and serve as a tumor suppressor. Though many studies were published to assess the relationship between p21 rs1059234 polymorphism and various cancer risks, there was no definite conclusion on this association. To derive a more precise quantitative assessment of the relationship, a large scale meta-analysis of 5,963 cases and 8,405 controls from 16 eligible published case-control studies was performed. Our analysis suggested that rs1059234 was not associated with the integral cancer risk for both dominant model [(T/T+C/T) vs C/C, OR=1.00, 95% CI: 0.84-1.18] and recessive model [T/T vs (C/C+C/T), OR=1.03, 95% CI: 0.93-1.15)]. However, further stratified analysis showed rs1059234 was greatly associated with the risk of squamous cell carcinoma of head and neck (SCCHN). Thus, larger scale primary studies are still required to further evaluate the interaction of p21 rs1059234 polymorphism and cancer risk in specific cancer subtypes.

Quantitative option analysis for implementation and management of landfills.

PubMed

Kerestecioğlu, Merih

2016-09-01

The selection of the most feasible strategy for implementation of landfills is a challenging step. Potential implementation options of landfills cover a wide range, from conventional construction contracts to the concessions. Montenegro, seeking to improve the efficiency of the public services while maintaining affordability, was considering privatisation as a way to reduce public spending on service provision. In this study, to determine the most feasible model for construction and operation of a regional landfill, a quantitative risk analysis was implemented with four steps: (i) development of a global risk matrix; (ii) assignment of qualitative probabilities of occurrences and magnitude of impacts; (iii) determination of the risks to be mitigated, monitored, controlled or ignored; (iv) reduction of the main risk elements; and (v) incorporation of quantitative estimates of probability of occurrence and expected impact for each risk element in the reduced risk matrix. The evaluated scenarios were: (i) construction and operation of the regional landfill by the public sector; (ii) construction and operation of the landfill by private sector and transfer of the ownership to the public sector after a pre-defined period; and (iii) operation of the landfill by the private sector, without ownership. The quantitative risk assessment concluded that introduction of a public private partnership is not the most feasible option, unlike the common belief in several public institutions in developing countries. A management contract for the first years of operation was advised to be implemented, after which, a long term operating contract may follow. © The Author(s) 2016.

Deployment of e-health services - a business model engineering strategy.

PubMed

Kijl, Björn; Nieuwenhuis, Lambert J M; Huis in 't Veld, Rianne M H A; Hermens, Hermie J; Vollenbroek-Hutten, Miriam M R

2010-01-01

We designed a business model for deploying a myofeedback-based teletreatment service. An iterative and combined qualitative and quantitative action design approach was used for developing the business model and the related value network. Insights from surveys, desk research, expert interviews, workshops and quantitative modelling were combined to produce the first business model and then to refine it in three design cycles. The business model engineering strategy provided important insights which led to an improved, more viable and feasible business model and related value network design. Based on this experience, we conclude that the process of early stage business model engineering reduces risk and produces substantial savings in costs and resources related to service deployment.

Quantitative analysis of [99mTc]C2A-GST distribution in the area at risk after myocardial ischemia and reperfusion using a compartmental model.

PubMed

Audi, Said; Poellmann, Michael; Zhu, Xiaoguang; Li, Zhixin; Zhao, Ming

2007-11-01

It was recently demonstrated that the radiolabeled C2A domain of synaptotagmin I accumulates avidly in the area at risk after ischemia and reperfusion. The objective was to quantitatively characterize the dynamic uptake of radiolabeled C2A in normal and ischemically injured myocardia using a compartmental model. To induce acute myocardial infarction, the left descending coronary artery was ligated for 18 min, followed by reperfusion. [99mTc]C2A-GST or its inactivated form, [99mTc]C2A-GST-NHS, was injected intravenously at 2 h after reperfusion. A group of four rats was sacrificed at 10, 30, 60 and 180 after injection. Uptake of [99mTc]C2A-GST and [99mTc]C2A-GST-NHS in the area at risk and in the normal myocardium were determined by gamma counting. A compartmental model was developed to quantitatively interpret myocardial uptake kinetic data. The model consists of two physical spaces (vascular space and tissue space), with plasma activity as input. The model allows for [99mTc]C2A-GST and [99mTc]C2A-GST-NHS diffusion between vascular and tissue spaces, as well as for [99mTc]C2A-GST sequestration in vascular and tissue spaces via specific binding. [99mTc]C2A-GST uptake in the area at risk was significantly higher than that for [99mTc]C2A-GST-NHS at all time points. The compartmental model separated [99mTc]C2A-GST uptake in the area at risk due to passive retention from that due to specific binding. The maximum amount of [99mTc]C2A-GST that could be sequestered in the area at risk due to specific binding was estimated at a total of 0.048 nmol/g tissue. The rate of [99mTc]C2A-GST sequestration within the tissue space of the area at risk was 0.012 ml/min. Modeling results also revealed that the diffusion rate of radiotracer between vascular and tissue spaces is the limiting factor of [99mTc]C2A-GST sequestration within the tissue space of the area at risk. [99mTc]C2A-GST is sequestered in the ischemically injured myocardium in a well-defined dynamic profile. Model parameters will be valuable indicators for gauging and guiding the development of future-generation molecular probes.

Understanding Pre-Quantitative Risk in Projects

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.

2011-01-01

Standard approaches to risk management in projects depend on the ability of teams to identify risks and quantify the probabilities and consequences of these risks (e.g., the 5 x 5 risk matrix). However, long before quantification does - or even can - occur, and long after, teams make decisions based on their pre-quantitative understanding of risk. These decisions can have long-lasting impacts on the project. While significant research has looked at the process of how to quantify risk, our understanding of how teams conceive of and manage pre-quantitative risk is lacking. This paper introduces the concept of pre-quantitative risk and discusses the implications of addressing pre-quantitative risk in projects.

Estimation and evaluation of management options to control and/or reduce the risk of not complying with commercial sterility.

PubMed

Pujol, Laure; Albert, Isabelle; Magras, Catherine; Johnson, Nicholas Brian; Membré, Jeanne-Marie

2015-11-20

In a previous study, a modular process risk model, from the raw material reception to the final product storage, was built to estimate the risk of a UHT-aseptic line of not complying with commercial sterility (Pujol et al., 2015). This present study was focused on demonstrating how the model (updated version with uncertainty and variability separated and 2(nd) order Monte Carlo procedure run) could be used to assess quantitatively the influence of management options. This assessment was done in three steps: pinpoint which process step had the highest influence on the risk, identify which management option(s) could be the most effective to control and/or reduce the risk, and finally evaluate quantitatively the influence of changing process setting(s) on the risk. For Bacillus cereus, it was identified that during post-process storage in an aseptic tank, there was potentially an air re-contamination due to filter efficiency loss (efficiency loss due to successive in-place sterilizations after cleaning operations), followed by B. cereus growth. Two options were then evaluated: i) reducing by one fifth of the number of filter sterilizations before renewing the filters, ii) designing new UHT-aseptic lines without an aseptic tank, i.e. without a storage period after the thermal process and before filling. Considering the uncertainty in the model, it was not possible to confirm whether these options had a significant influence on the risk associated with B. cereus. On the other hand, for Geobacillus stearothermophilus, combinations of heat-treatment time and temperature enabling the control or reduction in risk by a factor of ca. 100 were determined; for ease of operational implementation, they were presented graphically in the form of iso-risk curves. For instance, it was established that a heat treatment of 138°C for 31s (instead of 138°C for 25s) enabled a reduction in risk to 18×10(-8) (95% CI=[10; 34]×10(-8)), instead of 578×10(-8) (95% CI=[429; 754]×10(-8)) initially. In conclusion, a modular risk model, as the one exemplified here with a UHT-aseptic line, is a valuable tool in process design and operation, bringing definitive quantitative elements into the decision making process. Copyright © 2015 Elsevier B.V. All rights reserved.

Influence of safety measures on the risks of transporting dangerous goods through road tunnels.

PubMed

Saccomanno, Frank; Haastrup, Palle

2002-12-01

Quantitative risk assessment (QRA) models are used to estimate the risks of transporting dangerous goods and to assess the merits of introducing alternative risk reduction measures for different transportation scenarios and assumptions. A comprehensive QRA model recently was developed in Europe for application to road tunnels. This model can assess the merits of a limited number of "native safety measures." In this article, we introduce a procedure for extending its scope to include the treatment of a number of important "nonnative safety measures" of interest to tunnel operators and decisionmakers. Nonnative safety measures were not included in the original model specification. The suggested procedure makes use of expert judgment and Monte Carlo simulation methods to model uncertainty in the revised risk estimates. The results of a case study application are presented that involve the risks of transporting a given volume of flammable liquid through a 10-km road tunnel.

A Comprehensive Review of Existing Risk Assessment Models in Cloud Computing

NASA Astrophysics Data System (ADS)

Amini, Ahmad; Jamil, Norziana

2018-05-01

Cloud computing is a popular paradigm in information technology and computing as it offers numerous advantages in terms of economical saving and minimal management effort. Although elasticity and flexibility brings tremendous benefits, it still raises many information security issues due to its unique characteristic that allows ubiquitous computing. Therefore, the vulnerabilities and threats in cloud computing have to be identified and proper risk assessment mechanism has to be in place for better cloud computing management. Various quantitative and qualitative risk assessment models have been proposed but up to our knowledge, none of them is suitable for cloud computing environment. This paper, we compare and analyse the strengths and weaknesses of existing risk assessment models. We then propose a new risk assessment model that sufficiently address all the characteristics of cloud computing, which was not appeared in the existing models.

Comparative-effectiveness research to aid population decision making by relating clinical outcomes and quality-adjusted life years.

PubMed

Campbell, Jonathan D; Zerzan, Judy; Garrison, Louis P; Libby, Anne M

2013-04-01

Comparative-effectiveness research (CER) at the population level is missing standardized approaches to quantify and weigh interventions in terms of their clinical risks, benefits, and uncertainty. We proposed an adapted CER framework for population decision making, provided example displays of the outputs, and discussed the implications for population decision makers. Building on decision-analytical modeling but excluding cost, we proposed a 2-step approach to CER that explicitly compared interventions in terms of clinical risks and benefits and linked this evidence to the quality-adjusted life year (QALY). The first step was a traditional intervention-specific evidence synthesis of risks and benefits. The second step was a decision-analytical model to simulate intervention-specific progression of disease over an appropriate time. The output was the ability to compare and quantitatively link clinical outcomes with QALYs. The outputs from these CER models include clinical risks, benefits, and QALYs over flexible and relevant time horizons. This approach yields an explicit, structured, and consistent quantitative framework to weigh all relevant clinical measures. Population decision makers can use this modeling framework and QALYs to aid in their judgment of the individual and collective risks and benefits of the alternatives over time. Future research should study effective communication of these domains for stakeholders. Copyright © 2013 Elsevier HS Journals, Inc. All rights reserved.

Quantifying the Risk of Human Toxoplasma gondii Infection Due to Consumption of Domestically Produced Lamb in the United States.

PubMed

Guo, Miao; Mishra, Abhinav; Buchanan, Robert L; Dubey, Jitender P; Hill, Dolores E; Gamble, H Ray; Pradhan, Abani K

2016-07-01

Toxoplasma gondii is a prevalent protozoan parasite worldwide. Human toxoplasmosis is responsible for considerable morbidity and mortality in the United States, and meat products have been identified as an important source of T. gondii infections in humans. The goal of this study was to develop a farm-to-table quantitative microbial risk assessment model to predict the public health burden in the United States associated with consumption of U.S. domestically produced lamb. T. gondii prevalence in market lambs was pooled from the 2011 National Animal Health Monitoring System survey, and the concentration of the infectious life stage (bradyzoites) was calculated in the developed model. A log-linear regression and an exponential doseresponse model were used to model the reduction of T. gondii during home cooking and to predict the probability of infection, respectively. The mean probability of infection per serving of lamb was estimated to be 1.5 cases per 100,000 servings, corresponding to ∼6,300 new infections per year in the U.S. Based on the sensitivity analysis, we identified cooking as the most effective method to influence human health risk. This study provided a quantitative microbial risk assessment framework for T. gondii infection through consumption of lamb and quantified the infection risk and public health burden associated with lamb consumption.

Risk-Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk.

PubMed

Tenenhaus-Aziza, Fanny; Daudin, Jean-Jacques; Maffre, Alexandre; Sanaa, Moez

2014-01-01

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk-based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo-contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What-if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures. © 2013 Society for Risk Analysis.

Quantitative Microbial Risk Assessment for Spray Irrigation of Dairy Manure Based on an Empirical Fate and Transport Model

PubMed Central

Burch, Tucker R.; Spencer, Susan K.; Stokdyk, Joel P.; Kieke, Burney A.; Larson, Rebecca A.; Firnstahl, Aaron D.; Rule, Ana M.

2017-01-01

Background: Spray irrigation for land-applying livestock manure is increasing in the United States as farms become larger and economies of scale make manure irrigation affordable. Human health risks from exposure to zoonotic pathogens aerosolized during manure irrigation are not well understood. Objectives: We aimed to a) estimate human health risks due to aerosolized zoonotic pathogens downwind of spray-irrigated dairy manure; and b) determine which factors (e.g., distance, weather conditions) have the greatest influence on risk estimates. Methods: We sampled downwind air concentrations of manure-borne fecal indicators and zoonotic pathogens during 21 full-scale dairy manure irrigation events at three farms. We fit these data to hierarchical empirical models and used model outputs in a quantitative microbial risk assessment (QMRA) to estimate risk [probability of acute gastrointestinal illness (AGI)] for individuals exposed to spray-irrigated dairy manure containing Campylobacter jejuni, enterohemorrhagic Escherichia coli (EHEC), or Salmonella spp. Results: Median risk estimates from Monte Carlo simulations ranged from 10−5 to 10−2 and decreased with distance from the source. Risk estimates for Salmonella or EHEC-related AGI were most sensitive to the assumed level of pathogen prevalence in dairy manure, while risk estimates for C. jejuni were not sensitive to any single variable. Airborne microbe concentrations were negatively associated with distance and positively associated with wind speed, both of which were retained in models as a significant predictor more often than relative humidity, solar irradiation, or temperature. Conclusions: Our model-based estimates suggest that reducing pathogen prevalence and concentration in source manure would reduce the risk of AGI from exposure to manure irrigation, and that increasing the distance from irrigated manure (i.e., setbacks) and limiting irrigation to times of low wind speed may also reduce risk. https://doi.org/10.1289/EHP283 PMID:28885976

Quantitative microbial risk assessment for spray irrigation of dairy manure based on an empirical fate and transport model

USGS Publications Warehouse

Burch, Tucker R; Spencer, Susan K.; Stokdyk, Joel; Kieke, Burney A; Larson, Rebecca A; Firnstahl, Aaron; Rule, Ana M; Borchardt, Mark A.

2017-01-01

BACKGROUND: Spray irrigation for land-applying livestock manure is increasing in the United States as farms become larger and economies of scale make manure irrigation affordable. Human health risks from exposure to zoonotic pathogens aerosolized during manure irrigation are not well understood. OBJECTIVES: We aimed to a) estimate human health risks due to aerosolized zoonotic pathogens downwind of spray-irrigated dairy manure; and b) determine which factors (e.g., distance, weather conditions) have the greatest influence on risk estimates. METHODS: We sampled downwind air concentrations of manure-borne fecal indicators and zoonotic pathogens during 21 full-scale dairy manure irri- gation events at three farms. We fit these data to hierarchical empirical models and used model outputs in a quantitative microbial risk assessment (QMRA) to estimate risk [probability of acute gastrointestinal illness (AGI)] for individuals exposed to spray-irrigated dairy manure containing Campylobacter jejuni, enterohemorrhagic Escherichia coli (EHEC), or Salmonella spp. RESULTS: Median risk estimates from Monte Carlo simulations ranged from 10−5 to 10−2 and decreased with distance from the source. Risk estimates for Salmonella or EHEC-related AGI were most sensitive to the assumed level of pathogen prevalence in dairy manure, while risk estimates for C. jejuni were not sensitive to any single variable. Airborne microbe concentrations were negatively associated with distance and positively associated with wind speed, both of which were retained in models as a significant predictor more often than relative humidity, solar irradiation, or temperature. CONCLUSIONS: Our model-based estimates suggest that reducing pathogen prevalence and concentration in source manure would reduce the risk of AGI from exposure to manure irrigation, and that increasing the distance from irrigated manure (i.e., setbacks) and limiting irrigation to times of low wind speed may also reduce risk.

A vision and strategy for exposure modelling at the U.S. EPA Office of Research and Development

EPA Science Inventory

Traditional, hazard-driven, single-chemical risk assessment practices cannot keep pace with the vast and growing numbers of chemicals in commerce. A well-defined, quantitative, and defensible means of identifying those with the greatest risk potential is needed, with exposure con...

Impact of Hydrogeological Uncertainty on Estimation of Environmental Risks Posed by Hydrocarbon Transportation Networks

NASA Astrophysics Data System (ADS)

Ciriello, V.; Lauriola, I.; Bonvicini, S.; Cozzani, V.; Di Federico, V.; Tartakovsky, Daniel M.

2017-11-01

Ubiquitous hydrogeological uncertainty undermines the veracity of quantitative predictions of soil and groundwater contamination due to accidental hydrocarbon spills from onshore pipelines. Such predictions, therefore, must be accompanied by quantification of predictive uncertainty, especially when they are used for environmental risk assessment. We quantify the impact of parametric uncertainty on quantitative forecasting of temporal evolution of two key risk indices, volumes of unsaturated and saturated soil contaminated by a surface spill of light nonaqueous-phase liquids. This is accomplished by treating the relevant uncertain parameters as random variables and deploying two alternative probabilistic models to estimate their effect on predictive uncertainty. A physics-based model is solved with a stochastic collocation method and is supplemented by a global sensitivity analysis. A second model represents the quantities of interest as polynomials of random inputs and has a virtually negligible computational cost, which enables one to explore any number of risk-related contamination scenarios. For a typical oil-spill scenario, our method can be used to identify key flow and transport parameters affecting the risk indices, to elucidate texture-dependent behavior of different soils, and to evaluate, with a degree of confidence specified by the decision-maker, the extent of contamination and the correspondent remediation costs.

Quantitative modelling of amyloidogenic processing and its influence by SORLA in Alzheimer's disease.

PubMed

Schmidt, Vanessa; Baum, Katharina; Lao, Angelyn; Rateitschak, Katja; Schmitz, Yvonne; Teichmann, Anke; Wiesner, Burkhard; Petersen, Claus Munck; Nykjaer, Anders; Wolf, Jana; Wolkenhauer, Olaf; Willnow, Thomas E

2012-01-04

The extent of proteolytic processing of the amyloid precursor protein (APP) into neurotoxic amyloid-β (Aβ) peptides is central to the pathology of Alzheimer's disease (AD). Accordingly, modifiers that increase Aβ production rates are risk factors in the sporadic form of AD. In a novel systems biology approach, we combined quantitative biochemical studies with mathematical modelling to establish a kinetic model of amyloidogenic processing, and to evaluate the influence by SORLA/SORL1, an inhibitor of APP processing and important genetic risk factor. Contrary to previous hypotheses, our studies demonstrate that secretases represent allosteric enzymes that require cooperativity by APP oligomerization for efficient processing. Cooperativity enables swift adaptive changes in secretase activity with even small alterations in APP concentration. We also show that SORLA prevents APP oligomerization both in cultured cells and in the brain in vivo, eliminating the preferred form of the substrate and causing secretases to switch to a less efficient non-allosteric mode of action. These data represent the first mathematical description of the contribution of genetic risk factors to AD substantiating the relevance of subtle changes in SORLA levels for amyloidogenic processing as proposed for patients carrying SORL1 risk alleles.

Quantitative modelling of amyloidogenic processing and its influence by SORLA in Alzheimer's disease

PubMed Central

Schmidt, Vanessa; Baum, Katharina; Lao, Angelyn; Rateitschak, Katja; Schmitz, Yvonne; Teichmann, Anke; Wiesner, Burkhard; Petersen, Claus Munck; Nykjaer, Anders; Wolf, Jana; Wolkenhauer, Olaf; Willnow, Thomas E

2012-01-01

The extent of proteolytic processing of the amyloid precursor protein (APP) into neurotoxic amyloid-β (Aβ) peptides is central to the pathology of Alzheimer's disease (AD). Accordingly, modifiers that increase Aβ production rates are risk factors in the sporadic form of AD. In a novel systems biology approach, we combined quantitative biochemical studies with mathematical modelling to establish a kinetic model of amyloidogenic processing, and to evaluate the influence by SORLA/SORL1, an inhibitor of APP processing and important genetic risk factor. Contrary to previous hypotheses, our studies demonstrate that secretases represent allosteric enzymes that require cooperativity by APP oligomerization for efficient processing. Cooperativity enables swift adaptive changes in secretase activity with even small alterations in APP concentration. We also show that SORLA prevents APP oligomerization both in cultured cells and in the brain in vivo, eliminating the preferred form of the substrate and causing secretases to switch to a less efficient non-allosteric mode of action. These data represent the first mathematical description of the contribution of genetic risk factors to AD substantiating the relevance of subtle changes in SORLA levels for amyloidogenic processing as proposed for patients carrying SORL1 risk alleles. PMID:21989385

Quantitative microbiological risk assessment as a tool to obtain useful information for risk managers--specific application to Listeria monocytogenes and ready-to-eat meat products.

PubMed

Mataragas, M; Zwietering, M H; Skandamis, P N; Drosinos, E H

2010-07-31

The presence of Listeria monocytogenes in a sliced cooked, cured ham-like meat product was quantitatively assessed. Sliced cooked, cured meat products are considered as high risk products. These ready-to-eat, RTE, products (no special preparation, e.g. thermal treatment, before eating is required), support growth of pathogens (high initial pH=6.2-6.4 and water activity=0.98-0.99) and has a relatively long period of storage at chilled temperatures with a shelf life equal to 60 days based on manufacturer's instructions. Therefore, in case of post-process contamination, even with low number of cells, the microorganism is able to reach unacceptable levels at the time of consumption. The aim of this study was to conduct a Quantitative Microbiological Risk Assessment (QMRA) on the risk of L. monocytogenes presence in RTE meat products. This may help risk managers to make decisions and apply control measures with ultimate objective the food safety assurance. Examples are given to illustrate the development of practical risk management strategies based on the results obtained from the QMRA model specifically developed for this pathogen/food product combination. Copyright 2010 Elsevier B.V. All rights reserved.

Household physical activity and cancer risk: a systematic review and dose-response meta-analysis of epidemiological studies

PubMed Central

Shi, Yun; Li, Tingting; Wang, Ying; Zhou, Lingling; Qin, Qin; Yin, Jieyun; Wei, Sheng; Liu, Li; Nie, Shaofa

2015-01-01

Controversial results of the association between household physical activity and cancer risk were reported among previous epidemiological studies. We conducted a meta-analysis to investigate the relationship of household physical activity and cancer risk quantitatively, especially in dose-response manner. PubMed, Embase, Web of science and the Cochrane Library were searched for cohort or case-control studies that examined the association between household physical activity and cancer risks. Random–effect models were conducted to estimate the summary relative risks (RRs), nonlinear or linear dose–response meta-analyses were performed to estimate the trend from the correlated log RR estimates across levels of household physical activity quantitatively. Totally, 30 studies including 41 comparisons met the inclusion criteria. Total cancer risks were reduced 16% among the people with highest household physical activity compared to those with lowest household physical activity (RR = 0.84, 95% CI = 0.76–0.93). The dose-response analyses indicated an inverse linear association between household physical activity and cancer risk. The relative risk was 0.98 (95% CI = 0.97–1.00) for per additional 10 MET-hours/week and it was 0.99 (95% CI = 0.98–0.99) for per 1 hour/week increase. These findings provide quantitative data supporting household physical activity is associated with decreased cancer risk in dose-response effect. PMID:26443426

A generalised individual-based algorithm for modelling the evolution of quantitative herbicide resistance in arable weed populations.

PubMed

Liu, Chun; Bridges, Melissa E; Kaundun, Shiv S; Glasgow, Les; Owen, Micheal Dk; Neve, Paul

2017-02-01

Simulation models are useful tools for predicting and comparing the risk of herbicide resistance in weed populations under different management strategies. Most existing models assume a monogenic mechanism governing herbicide resistance evolution. However, growing evidence suggests that herbicide resistance is often inherited in a polygenic or quantitative fashion. Therefore, we constructed a generalised modelling framework to simulate the evolution of quantitative herbicide resistance in summer annual weeds. Real-field management parameters based on Amaranthus tuberculatus (Moq.) Sauer (syn. rudis) control with glyphosate and mesotrione in Midwestern US maize-soybean agroecosystems demonstrated that the model can represent evolved herbicide resistance in realistic timescales. Sensitivity analyses showed that genetic and management parameters were impactful on the rate of quantitative herbicide resistance evolution, whilst biological parameters such as emergence and seed bank mortality were less important. The simulation model provides a robust and widely applicable framework for predicting the evolution of quantitative herbicide resistance in summer annual weed populations. The sensitivity analyses identified weed characteristics that would favour herbicide resistance evolution, including high annual fecundity, large resistance phenotypic variance and pre-existing herbicide resistance. Implications for herbicide resistance management and potential use of the model are discussed. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

An Online Risk Monitor System (ORMS) to Increase Safety and Security Levels in Industry

NASA Astrophysics Data System (ADS)

Zubair, M.; Rahman, Khalil Ur; Hassan, Mehmood Ul

2013-12-01

The main idea of this research is to develop an Online Risk Monitor System (ORMS) based on Living Probabilistic Safety Assessment (LPSA). The article highlights the essential features and functions of ORMS. The basic models and modules such as, Reliability Data Update Model (RDUM), running time update, redundant system unavailability update, Engineered Safety Features (ESF) unavailability update and general system update have been described in this study. ORMS not only provides quantitative analysis but also highlights qualitative aspects of risk measures. ORMS is capable of automatically updating the online risk models and reliability parameters of equipment. ORMS can support in the decision making process of operators and managers in Nuclear Power Plants.

Non-animal approaches for toxicokinetics in risk evaluations of food chemicals.

PubMed

Punt, Ans; Peijnenburg, Ad A C M; Hoogenboom, Ron L A P; Bouwmeester, Hans

2017-01-01

The objective of the present work was to review the availability and predictive value of non-animal toxicokinetic approaches and to evaluate their current use in European risk evaluations of food contaminants, additives and food contact materials, as well as pesticides and medicines. Results revealed little use of quantitative animal or human kinetic data in risk evaluations of food chemicals, compared with pesticides and medicines. Risk evaluations of medicines provided sufficient in vivo kinetic data from different species to evaluate the predictive value of animal kinetic data for humans. These data showed a relatively poor correlation between the in vivo bioavailability in rats and dogs versus that in humans. In contrast, in vitro (human) kinetic data have been demonstrated to provide adequate predictions of the fate of compounds in humans, using appropriate in vitro-in vivo scalers and by integration of in vitro kinetic data with in silico kinetic modelling. Even though in vitro kinetic data were found to be occasionally included within risk evaluations of food chemicals, particularly results from Caco-2 absorption experiments and in vitro data on gut-microbial conversions, only minor use of in vitro methods for metabolism and quantitative in vitro-in vivo extrapolation methods was identified. Yet, such quantitative predictions are essential in the development of alternatives to animal testing as well as to increase human relevance of toxicological risk evaluations. Future research should aim at further improving and validating quantitative alternative methods for kinetics, thereby increasing regulatory acceptance of non-animal kinetic data.

Physiologically based pharmacokinetic (PBPK) modeling considering methylated trivalent arsenicals

EPA Science Inventory

PBPK modeling provides a quantitative biologically-based framework to integrate diverse types of information for application to risk analysis. For example, genetic polymorphisms in arsenic metabolizing enzymes (AS3MT) can lead to differences in target tissue dosimetry for key tri...

A quantitative risk assessment model to evaluate effective border control measures for rabies prevention.

PubMed

Weng, Hsin-Yi; Wu, Pei-I; Yang, Ping-Cheng; Tsai, Yi-Lun; Chang, Chao-Chin

2010-01-01

Border control is the primary method to prevent rabies emergence. This study developed a quantitative risk model incorporating stochastic processes to evaluate whether border control measures could efficiently prevent rabies introduction through importation of cats and dogs using Taiwan as an example. Both legal importation and illegal smuggling were investigated. The impacts of reduced quarantine and/or waiting period on the risk of rabies introduction were also evaluated. The results showed that Taiwan's current animal importation policy could effectively prevent rabies introduction through legal importation of cats and dogs. The median risk of a rabid animal to penetrate current border control measures and enter Taiwan was 5.33 x 10(-8) (95th percentile: 3.20 x 10(-7)). However, illegal smuggling may pose Taiwan to the great risk of rabies emergence. Reduction of quarantine and/or waiting period would affect the risk differently, depending on the applied assumptions, such as increased vaccination coverage, enforced custom checking, and/or change in number of legal importations. Although the changes in the estimated risk under the assumed alternatives were not substantial except for completely abolishing quarantine, the consequences of rabies introduction may yet be considered to be significant in a rabies-free area. Therefore, a comprehensive benefit-cost analysis needs to be conducted before recommending these alternative measures.

Clinical and physiological assessments for elucidating falls risk in Parkinson's disease.

PubMed

Latt, Mark D; Lord, Stephen R; Morris, John G L; Fung, Victor S C

2009-07-15

The study aims were to devise (1) a fall risk screen for people with PD using routine clinical measures and (2) an explanatory (physiological) fall risk assessment for guiding fall prevention interventions. One hundred thirteen people with PD (age 66 +/- 95% CI 1.6 years) underwent clinical assessments and quantitative tests of sway, gait, strength, reaction time, and lower limb sensation. Participants were then followed up for 12 months to determine fall incidence. In the follow-up year, 51 participants (45%) fell one or more times whereas 62 participants (55%) did not fall. Multivariate analyses of routine clinical measures revealed that a fall in the past year, abnormal axial posture, cognitive impairment, and freezing of gait were independent risk factors for falls and predicted 38/51 fallers (75%) and 45/62 non-fallers (73%). A multivariate model combining clinical and physiological measures that elucidate the pathophysiology of falls identified abnormal posture, freezing of gait, frontal impairment, poor leaning balance, and leg weakness as independent risk factors. This model correctly classified 39/51 fallers (77%) and 51/62 non-fallers (82%). Patients with PD at risk of falls can be identified accurately with routine clinical assessments and quantitative physiological tests. Many of the risk factors identified are amenable to targeted intervention. 2009 Movement Disorder Society.

Assessing Risks to Sea Otters and the Exxon Valdez Oil Spill: New Scenarios, Attributable Risk, and Recovery

PubMed Central

Harwell, Mark A.; Gentile, John H.

2014-01-01

The Exxon Valdez oil spill occurred more than two decades ago, and the Prince William Sound ecosystem has essentially recovered. Nevertheless, discussion continues on whether or not localized effects persist on sea otters (Enhydra lutris) at northern Knight Island (NKI) and, if so, what are the associated attributable risks. A recent study estimated new rates of sea otter encounters with subsurface oil residues (SSOR) from the oil spill. We previously demonstrated that a potential pathway existed for exposures to polycyclic aromatic hydrocarbons (PAHs) and conducted a quantitative ecological risk assessment using an individual-based model that simulated this and other plausible exposure pathways. Here we quantitatively update the potential for this exposure pathway to constitute an ongoing risk to sea otters using the new estimates of SSOR encounters. Our conservative model predicted that the assimilated doses of PAHs to the 1-in-1000th most-exposed sea otters would remain 1–2 orders of magnitude below the chronic effects thresholds. We re-examine the baseline estimates, post-spill surveys, recovery status, and attributable risks for this subpopulation. We conclude that the new estimated frequencies of encountering SSOR do not constitute a plausible risk for sea otters at NKI and these sea otters have fully recovered from the oil spill. PMID:24587690

The Use of Mode of Action Information in Risk Assessment: Quantitative Key Events/Dose-Response Framework for Modeling the Dose-Response for Key Events

EPA Science Inventory

The HESI RISK21 project formed the Dose-Response/Mode-of-Action Subteam to develop strategies for using all available data (in vitro, in vivo, and in silico) to advance the next-generation of chemical risk assessments. A goal of the Subteam is to enhance the existing Mode of Act...

Modeling Payload Stowage Impacts on Fire Risks On-Board the International Space Station

NASA Technical Reports Server (NTRS)

Anton, Kellie e.; Brown, Patrick F.

2010-01-01

The purpose of this presentation is to determine the risks of fire on-board the ISS due to non-standard stowage. ISS stowage is constantly being reexamined for optimality. Non-standard stowage involves stowing items outside of rack drawers, and fire risk is a key concern and is heavily mitigated. A Methodology is needed to account for fire risk due to non-standard stowage to capture the risk. The contents include: 1) Fire Risk Background; 2) General Assumptions; 3) Modeling Techniques; 4) Event Sequence Diagram (ESD); 5) Qualitative Fire Analysis; 6) Sample Qualitative Results for Fire Risk; 7) Qualitative Stowage Analysis; 8) Sample Qualitative Results for Non-Standard Stowage; and 9) Quantitative Analysis Basic Event Data.

17 CFR 229.305 - (Item 305) Quantitative and qualitative disclosures about market risk.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 2 2011-04-01 2011-04-01 false (Item 305) Quantitative and... Information § 229.305 (Item 305) Quantitative and qualitative disclosures about market risk. (a) Quantitative information about market risk. (1) Registrants shall provide, in their reporting currency, quantitative...

17 CFR 229.305 - (Item 305) Quantitative and qualitative disclosures about market risk.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 17 Commodity and Securities Exchanges 2 2010-04-01 2010-04-01 false (Item 305) Quantitative and... Information § 229.305 (Item 305) Quantitative and qualitative disclosures about market risk. (a) Quantitative information about market risk. (1) Registrants shall provide, in their reporting currency, quantitative...

17 CFR 229.305 - (Item 305) Quantitative and qualitative disclosures about market risk.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 17 Commodity and Securities Exchanges 2 2013-04-01 2013-04-01 false (Item 305) Quantitative and... Information § 229.305 (Item 305) Quantitative and qualitative disclosures about market risk. (a) Quantitative information about market risk. (1) Registrants shall provide, in their reporting currency, quantitative...

17 CFR 229.305 - (Item 305) Quantitative and qualitative disclosures about market risk.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 17 Commodity and Securities Exchanges 3 2014-04-01 2014-04-01 false (Item 305) Quantitative and... Information § 229.305 (Item 305) Quantitative and qualitative disclosures about market risk. (a) Quantitative information about market risk. (1) Registrants shall provide, in their reporting currency, quantitative...

17 CFR 229.305 - (Item 305) Quantitative and qualitative disclosures about market risk.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 17 Commodity and Securities Exchanges 2 2012-04-01 2012-04-01 false (Item 305) Quantitative and... Information § 229.305 (Item 305) Quantitative and qualitative disclosures about market risk. (a) Quantitative information about market risk. (1) Registrants shall provide, in their reporting currency, quantitative...

Assessment of Yellow Fever Epidemic Risk: An Original Multi-criteria Modeling Approach

PubMed Central

Briand, Sylvie; Beresniak, Ariel; Nguyen, Tim; Yonli, Tajoua; Duru, Gerard; Kambire, Chantal; Perea, William

2009-01-01

Background Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in the early 1980s. We developed an original modeling approach to assess YF epidemic risk (vulnerability) and to prioritize the populations to be vaccinated. Methods and Findings We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with “exposure” to virus/vector and one with “susceptibility” of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns. Conclusion This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors. PMID:19597548

Assessment of yellow fever epidemic risk: an original multi-criteria modeling approach.

PubMed

Briand, Sylvie; Beresniak, Ariel; Nguyen, Tim; Yonli, Tajoua; Duru, Gerard; Kambire, Chantal; Perea, William

2009-07-14

Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in the early 1980s. We developed an original modeling approach to assess YF epidemic risk (vulnerability) and to prioritize the populations to be vaccinated. We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with "exposure" to virus/vector and one with "susceptibility" of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns. This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors.

Development of quantitative risk acceptance criteria

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

Griesmeyer, J. M.; Okrent, D.

Some of the major considerations for effective management of risk are discussed, with particular emphasis on risks due to nuclear power plant operations. Although there are impacts associated with the rest of the fuel cycle, they are not addressed here. Several previously published proposals for quantitative risk criteria are reviewed. They range from a simple acceptance criterion on individual risk of death to a quantitative risk management framework. The final section discussed some of the problems in the establishment of a framework for the quantitative management of risk.

Development of the Methodology Needed to Quantify Risks to Groundwater at CO2 Storage Sites

NASA Astrophysics Data System (ADS)

Brown, C. F.; Birkholzer, J. T.; Carroll, S.; Hakala, A.; Keating, E. H.; Lopano, C. L.; Newell, D. L.; Spycher, N.

2011-12-01

The National Risk Assessment Partnership (NRAP) is an effort that harnesses capabilities across five U.S. Department of Energy (DOE) national laboratories into a mission-focused platform to develop a defensible, science-based quantitative methodology for determining risk profiles at CO2 storage sites. NRAP is conducting risk and uncertainty analysis in the areas of reservoir performance, natural leakage pathways, wellbore integrity, groundwater protection, monitoring, and systems level modeling. The mission of NRAP is "to provide the scientific underpinning for risk assessment with respect to the long-term storage of CO2, including assessment of residual risk associated with a site post-closure." Additionally, NRAP will develop a strategic, risk-based monitoring protocol, such that monitoring at all stages of a project effectively minimizes uncertainty in the predicted behavior of the site, thereby increasing confidence in storage integrity. NRAP's research focus in the area of groundwater protection is divided into three main tasks: 1) development of quantitative risk profiles for potential groundwater impacts; 2) filling key science gaps in developing those risk profiles; and 3) field-based confirmation. Within these three tasks, researchers are engaged in collaborative studies to determine metrics to identify system perturbation and their associated risk factors. Reservoir simulations are being performed to understand/predict consequences of hypothetical leakage scenarios, from which reduced order models are being developed to feed risk profile development. Both laboratory-based experiments and reactive transport modeling studies provide estimates of geochemical impacts over a broad range of leakage scenarios. This presentation will provide an overview of the research objectives within NRAP's groundwater protection focus area, as well as select accomplishments achieved to date.

Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: a model in neuroblastoma.

PubMed

Tadeo, Irene; Piqueras, Marta; Montaner, David; Villamón, Eva; Berbegall, Ana P; Cañete, Adela; Navarro, Samuel; Noguera, Rosa

2014-02-01

Risk classification and treatment stratification for cancer patients is restricted by our incomplete picture of the complex and unknown interactions between the patient's organism and tumor tissues (transformed cells supported by tumor stroma). Moreover, all clinical factors and laboratory studies used to indicate treatment effectiveness and outcomes are by their nature a simplification of the biological system of cancer, and cannot yet incorporate all possible prognostic indicators. A multiparametric analysis on 184 tumor cylinders was performed. To highlight the benefit of integrating digitized medical imaging into this field, we present the results of computational studies carried out on quantitative measurements, taken from stromal and cancer cells and various extracellular matrix fibers interpenetrated by glycosaminoglycans, and eight current approaches to risk stratification systems in patients with primary and nonprimary neuroblastoma. New tumor tissue indicators from both fields, the cellular and the extracellular elements, emerge as reliable prognostic markers for risk stratification and could be used as molecular targets of specific therapies. The key to dealing with personalized therapy lies in the mathematical modeling. The use of bioinformatics in patient-tumor-microenvironment data management allows a predictive model in neuroblastoma.

Quantitative microbial risk assessment for spray irrigation of dairy manure based on an empirical fate and transport model

USDA-ARS?s Scientific Manuscript database

Background: Spray irrigation for land-applying livestock manure is increasing in the United States as farms become larger and economies of scale make manure irrigation affordable. However, human health risks from exposure to zoonotic pathogens aerosolized during manure irrigation are not well-unders...

US EPA - A*Star Partnership - Accelerating the Acceptance of Next-Generation Sciences and Their Application to Regulatory Risk Assessment (A*Star Symposium, Singapore)

EPA Science Inventory

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...

Application of in Vitro Biotransformation Data and Pharmacokinetic Modeling to Risk Assessment

EPA Science Inventory

The adverse biological effects of toxic substances are dependent upon the exposure concentration and the duration of exposure. Pharmacokinetic models can quantitatively relate the external concentration of a toxicant in the environment to the internal dose of the toxicant in the ...

20180312 - Structure-based QSAR Models to Predict Systemic Toxicity Points of Departure (SOT)

EPA Science Inventory

Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models base...

HOW CAN BIOLOGICALLY-BASED MODELING OF ARSENIC KINETICS AND DYNAMICS INFORM THE RISK ASSESSMENT PROCESS?

EPA Science Inventory

Quantitative biologically-based models describing key events in the continuum from arsenic exposure to the development of adverse health effects provide a framework to integrate information obtained across diverse research areas. For example, genetic polymorphisms in arsenic met...

Computational modeling of the amphibian thyroid axis supported by targeted in vivo testing to advance quantitative adverse outcome pathway development

EPA Science Inventory

In vitro screening of chemicals for bioactivity together with computational modeling are beginning to replace animal toxicity testing in support of chemical risk assessment. To facilitate this transition, an amphibian thyroid axis model has been developed to describe thyroid home...

Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods.

PubMed

Parkinson, Craig; Foley, Kieran; Whybra, Philip; Hills, Robert; Roberts, Ashley; Marshall, Chris; Staffurth, John; Spezi, Emiliano

2018-04-11

Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated. Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

76 FR 77543 - Quantitative Summary of the Benefits and Risks of Prescription Drugs: A Literature Review

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-13

...] Quantitative Summary of the Benefits and Risks of Prescription Drugs: A Literature Review AGENCY: Food and Drug... availability of a draft report entitled ``Quantitative Summary of the Benefits and Risks of Prescription Drugs... ``Quantitative Summary of the Benefits and Risks of Prescription Drugs: A Literature Review.'' A literature...

The Importance of Human Reliability Analysis in Human Space Flight: Understanding the Risks

NASA Technical Reports Server (NTRS)

Hamlin, Teri L.

2010-01-01

HRA is a method used to describe, qualitatively and quantitatively, the occurrence of human failures in the operation of complex systems that affect availability and reliability. Modeling human actions with their corresponding failure in a PRA (Probabilistic Risk Assessment) provides a more complete picture of the risk and risk contributions. A high quality HRA can provide valuable information on potential areas for improvement, including training, procedural, equipment design and need for automation.

Analysis of perceived risk among construction workers: a cross-cultural study and reflection on the Hofstede model.

PubMed

Martinez-Fiestas, Myriam; Rodríguez-Garzón, Ignacio; Delgado-Padial, Antonio; Lucas-Ruiz, Valeriano

2017-09-01

This article presents a cross-cultural study on perceived risk in the construction industry. Worker samples from three different countries were studied: Spain, Peru and Nicaragua. The main goal was to explain how construction workers perceive their occupational hazard and to analyze how this is related to their national culture. The model used to measure perceived risk was the psychometric paradigm. The results show three very similar profiles, indicating that risk perception is independent of nationality. A cultural analysis was conducted using the Hofstede model. The results of this analysis and the relation to perceived risk showed that risk perception in construction is independent of national culture. Finally, a multiple lineal regression analysis was conducted to determine what qualitative attributes could predict the global quantitative size of risk perception. All of the findings have important implications regarding the management of safety in the workplace.

Fuzzy-probabilistic model for risk assessment of radioactive material railway transportation.

PubMed

Avramenko, M; Bolyatko, V; Kosterev, V

2005-01-01

Transportation of radioactive materials is obviously accompanied by a certain risk. A model for risk assessment of emergency situations and terrorist attacks may be useful for choosing possible routes and for comparing the various defence strategies. In particular, risk assessment is crucial for safe transportation of excess weapons-grade plutonium arising from the removal of plutonium from military employment. A fuzzy-probabilistic model for risk assessment of railway transportation has been developed taking into account the different natures of risk-affecting parameters (probabilistic and not probabilistic but fuzzy). Fuzzy set theory methods as well as standard methods of probability theory have been used for quantitative risk assessment. Information-preserving transformations are applied to realise the correct aggregation of probabilistic and fuzzy parameters. Estimations have also been made of the inhalation doses resulting from possible accidents during plutonium transportation. The obtained data show the scale of possible consequences that may arise from plutonium transportation accidents.

A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks.

PubMed

Jing, Xu; Hu, Hanwen; Yang, Huijun; Au, Man Ho; Li, Shuqin; Xiong, Naixue; Imran, Muhammad; Vasilakos, Athanasios V

2017-03-21

The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider's server contains a lot of valuable resources. LoBSs' users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs' risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs' risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing.

A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks

PubMed Central

Jing, Xu; Hu, Hanwen; Yang, Huijun; Au, Man Ho; Li, Shuqin; Xiong, Naixue; Imran, Muhammad; Vasilakos, Athanasios V.

2017-01-01

The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing. PMID:28335569

Towards a better reliability of risk assessment: development of a qualitative & quantitative risk evaluation model (Q2REM) for different trades of construction works in Hong Kong.

PubMed

Fung, Ivan W H; Lo, Tommy Y; Tung, Karen C F

2012-09-01

Since the safety professionals are the key decision makers dealing with project safety and risk assessment in the construction industry, their perceptions of safety risk would directly affect the reliability of risk assessment. The safety professionals generally tend to heavily rely on their own past experiences to make subjective decisions on risk assessment without systematic decision making. Indeed, understanding of the underlying principles of risk assessment is significant. In this study, the qualitative analysis on the safety professionals' beliefs of risk assessment and their perceptions towards risk assessment, including their recognitions of possible accident causes, the degree of differentiations on their perceptions of risk levels of different trades of works, recognitions of the occurrence of different types of accidents, and their inter-relationships with safety performance in terms of accident rates will be explored in the Stage 1. At the second stage, the deficiencies of the current general practice for risk assessment can be sorted out firstly. Based on the findings from Stage 1 and the historical accident data from 15 large-scaled construction projects in 3-year average, a risk evaluation model prioritizing the risk levels of different trades of works and which cause different types of site accident due to various accident causes will be developed quantitatively. With the suggested systematic accident recording techniques, this model can be implemented in the construction industry at both project level and organizational level. The model (Q(2)REM) not only act as a useful supplementary guideline of risk assessment for the construction safety professionals, but also assists them to pinpoint the potential risks on site for the construction workers under respective trades of works through safety trainings and education. It, in turn, arouses their awareness on safety risk. As the Q(2)REM can clearly show the potential accident causes leading to different types of accident by trade of works, it helps the concerned safety professionals and parties to plan effective accident prevention measures with reference to the priority of the risk levels. Copyright © 2011 Elsevier Ltd. All rights reserved.

Quantification of uncertainty in flood risk assessment for flood protection planning: a Bayesian approach

NASA Astrophysics Data System (ADS)

Dittes, Beatrice; Špačková, Olga; Ebrahimian, Negin; Kaiser, Maria; Rieger, Wolfgang; Disse, Markus; Straub, Daniel

2017-04-01

Flood risk estimates are subject to significant uncertainties, e.g. due to limited records of historic flood events, uncertainty in flood modeling, uncertain impact of climate change or uncertainty in the exposure and loss estimates. In traditional design of flood protection systems, these uncertainties are typically just accounted for implicitly, based on engineering judgment. In the AdaptRisk project, we develop a fully quantitative framework for planning of flood protection systems under current and future uncertainties using quantitative pre-posterior Bayesian decision analysis. In this contribution, we focus on the quantification of the uncertainties and study their relative influence on the flood risk estimate and on the planning of flood protection systems. The following uncertainty components are included using a Bayesian approach: 1) inherent and statistical (i.e. limited record length) uncertainty; 2) climate uncertainty that can be learned from an ensemble of GCM-RCM models; 3) estimates of climate uncertainty components not covered in 2), such as bias correction, incomplete ensemble, local specifics not captured by the GCM-RCM models; 4) uncertainty in the inundation modelling; 5) uncertainty in damage estimation. We also investigate how these uncertainties are possibly reduced in the future when new evidence - such as new climate models, observed extreme events, and socio-economic data - becomes available. Finally, we look into how this new evidence influences the risk assessment and effectivity of flood protection systems. We demonstrate our methodology for a pre-alpine catchment in southern Germany: the Mangfall catchment in Bavaria that includes the city of Rosenheim, which suffered significant losses during the 2013 flood event.

Comparison of Global and Mode of Action-Based Models for Aquatic Toxicity

EPA Science Inventory

The ability to estimate aquatic toxicity for a wide variety of chemicals is a critical need for ecological risk assessment and chemical regulation. The consensus in the literature is that mode of action (MOA) based QSAR (Quantitative Structure Activity Relationship) models yield ...

How Can Biologically-Based Modeling of Arsenic Kinetics and Dynamics Inform the Risk Assessment Process? -- ETD

EPA Science Inventory

Quantitative biologically-based models describing key events in the continuum from arsenic exposure to the development of adverse health effects provide a framework to integrate information obtained across diverse research areas. For example, genetic polymorphisms in arsenic me...

Marine oil spill risk mapping for accidental pollution and its application in a coastal city.

PubMed

Lan, Dongdong; Liang, Bin; Bao, Chenguang; Ma, Minghui; Xu, Yan; Yu, Chunyan

2015-07-15

Accidental marine oil spill pollution can result in severe environmental, ecological, economic and other consequences. This paper discussed the model of Marine Oil Spill Risk Mapping (MOSRM), which was constructed as follows: (1) proposing a marine oil spill risk system based on the typical marine oil spill pollution accidents and prevailing risk theories; (2) identifying suitable indexes that are supported by quantitative sub-indexes; (3) constructing the risk measuring models according to the actual interactions between the factors in the risk system; and (4) assessing marine oil spill risk on coastal city scale with GIS to map the overall risk. The case study of accidental marine oil spill pollution in the coastal area of Dalian, China was used to demonstrate the effectiveness of the model. The coastal areas of Dalian were divided into three zones with risk degrees of high, medium, and low. And detailed countermeasures were proposed for specific risk zones. Copyright © 2015 Elsevier Ltd. All rights reserved.

Model-based approach for quantitative estimates of skin, heart, and lung toxicity risk for left-side photon and proton irradiation after breast-conserving surgery.

PubMed

Tommasino, Francesco; Durante, Marco; D'Avino, Vittoria; Liuzzi, Raffaele; Conson, Manuel; Farace, Paolo; Palma, Giuseppe; Schwarz, Marco; Cella, Laura; Pacelli, Roberto

2017-05-01

Proton beam therapy represents a promising modality for left-side breast cancer (BC) treatment, but concerns have been raised about skin toxicity and poor cosmesis. The aim of this study is to apply skin normal tissue complication probability (NTCP) model for intensity modulated proton therapy (IMPT) optimization in left-side BC. Ten left-side BC patients undergoing photon irradiation after breast-conserving surgery were randomly selected from our clinical database. Intensity modulated photon (IMRT) and IMPT plans were calculated with iso-tumor-coverage criteria and according to RTOG 1005 guidelines. Proton plans were computed with and without skin optimization. Published NTCP models were employed to estimate the risk of different toxicity endpoints for skin, lung, heart and its substructures. Acute skin NTCP evaluation suggests a lower toxicity level with IMPT compared to IMRT when the skin is included in proton optimization strategy (0.1% versus 1.7%, p < 0.001). Dosimetric results show that, with the same level of tumor coverage, IMPT attains significant heart and lung dose sparing compared with IMRT. By NTCP model-based analysis, an overall reduction in the cardiopulmonary toxicity risk prediction can be observed for all IMPT compared to IMRT plans: the relative risk reduction from protons varies between 0.1 and 0.7 depending on the considered toxicity endpoint. Our analysis suggests that IMPT might be safely applied without increasing the risk of severe acute radiation induced skin toxicity. The quantitative risk estimates also support the potential clinical benefits of IMPT for left-side BC irradiation due to lower risk of cardiac and pulmonary morbidity. The applied approach might be relevant on the long term for the setup of cost-effectiveness evaluation strategies based on NTCP predictions.

Quantitative Assessment of the Safety Benefits Associated with Increasing Clinical Peanut Thresholds Through Immunotherapy.

PubMed

Baumert, Joseph L; Taylor, Steve L; Koppelman, Stef J

Peanut immunotherapy studies are conducted with the aim to decrease the sensitivity of patients to peanut exposure with the outcome evaluated by testing the threshold for allergic response in a double-blind placebo-controlled food challenge. The clinical relevance of increasing this threshold is not well characterized. We aimed to quantify the clinical benefit of an increased threshold for peanut-allergic patients. Quantitative risk assessment was performed by matching modeled exposure to peanut protein with individual threshold levels. Exposure was modeled by pairing US consumption data for various food product categories with potential contamination levels of peanut that have been demonstrated to be present on occasion in such food products. Cookies, ice cream, doughnuts/snack cakes, and snack chip mixes were considered in the risk assessment. Increasing the baseline threshold before immunotherapy from 100 mg or less peanut protein to 300 mg peanut protein postimmunotherapy reduces the risk of experiencing an allergic reaction by more than 95% for all 4 food product categories that may contain trace levels of peanut residue. Further increase in the threshold to 1000 mg of peanut protein had an additional quantitative benefit in risk reduction for all patients reacting to 300 mg or less at baseline. We conclude that achieving thresholds of 300 mg and 1000 mg of peanut protein by peanut immunotherapy is clinically relevant, and that the risk for peanut-allergic patients who have achieved this increased threshold to experience an allergic reaction is reduced in a clinically meaningful way. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

A model for assessing the risk of human trafficking on a local level

NASA Astrophysics Data System (ADS)

Colegrove, Amanda

Human trafficking is a human rights violation that is difficult to quantify. Models for estimating the number of victims of trafficking presented by previous researchers depend on inconsistent, poor quality data. As an intermediate step to help current efforts by nonprofits to combat human trafficking, this project presents a model that is not dependent on quantitative data specific to human trafficking, but rather profiles the risk of human trafficking at the local level through causative factors. Businesses, indicated by the literature, were weighted based on the presence of characteristics that increase the likelihood of trafficking in persons. The mean risk was calculated by census tract to reveal the multiplicity of risk levels in both rural and urban settings. Results indicate that labor trafficking may be a more diffuse problem in Missouri than sex trafficking. Additionally, spatial patterns of risk remained largely the same regardless of adjustments made to the model.

Understanding the effects of different HIV transmission models in individual-based microsimulation of HIV epidemic dynamics in people who inject drugs

PubMed Central

MONTEIRO, J.F.G.; ESCUDERO, D.J.; WEINREB, C.; FLANIGAN, T.; GALEA, S.; FRIEDMAN, S.R.; MARSHALL, B.D.L.

2017-01-01

SUMMARY We investigated how different models of HIV transmission, and assumptions regarding the distribution of unprotected sex and syringe-sharing events (‘risk acts’), affect quantitative understanding of HIV transmission process in people who inject drugs (PWID). The individual-based model simulated HIV transmission in a dynamic sexual and injecting network representing New York City. We constructed four HIV transmission models: model 1, constant probabilities; model 2, random number of sexual and parenteral acts; model 3, viral load individual assigned; and model 4, two groups of partnerships (low and high risk). Overall, models with less heterogeneity were more sensitive to changes in numbers risk acts, producing HIV incidence up to four times higher than that empirically observed. Although all models overestimated HIV incidence, micro-simulations with greater heterogeneity in the HIV transmission modelling process produced more robust results and better reproduced empirical epidemic dynamics. PMID:26753627

Quantitative risk assessment for Escherichia coli O157:H7 in frozen ground beef patties consumed by young children in French households.

PubMed

Delignette-Muller, M L; Cornu, M

2008-11-30

A quantitative risk assessment for Escherichia coli O157:H7 in frozen ground beef patties consumed by children under 10 years of age in French households was conducted by a national study group describing an outbreak which occurred in France in 2005. Our exposure assessment model incorporates results from French surveys on consumption frequency of ground beef patties, serving size and consumption preference, microbial destruction experiments and microbial counts on patties sampled from the industrial batch which were responsible for the outbreak. Two different exposure models were proposed, respectively for children under the age of 5 and for children between 5 and 10 years. For each of these two age groups, a single-hit dose-response model was proposed to describe the probability of hemolytic and uremic syndrome (HUS) as a function of the ingested dose. For each group, the single parameter of this model was estimated by Bayesian inference, using the results of the exposure assessment and the epidemiological data collected during the outbreak. Results show that children under 5 years of age are roughly 5 times more susceptible to the pathogen than children over 5 years. Exposure and dose-response models were used in a scenario analysis in order to validate the use of the model and to propose appropriate guidelines in order to prevent new outbreaks. The impact of the cooking preference was evaluated, showing that only a well-done cooking notably reduces the HUS risk, without annulling it. For each age group, a relation between the mean individual HUS risk per serving and the contamination level in a ground beef batch was proposed, as a tool to help French risk managers.

Does Psychosocial Work Environment Factors Predict Stress and Mean Arterial Pressure in the Malaysian Industry Workers?

PubMed

Javaid, Muhammad Umair; Isha, Ahmad Shahrul Nizam; Sabir, Asrar Ahmed; Ghazali, Zulkipli; Nübling, Matthias

2018-01-01

Psychosocial risks are considered as a burning issue in the Asia-Pacific region. The aim of this study was to investigate the impact of psychosocial work environment factors on health of petrochemical industry workers of Malaysia. In lieu to job demands-resources theory, significant positive associations were found between quantitative demands, work-family conflict, and job insecurity with stress, while a significant negative association of role clarity as a resource factor with stress was detected. We also found that quantitative demands were significantly associated with the mean arterial pressure (MAP). Multistage sampling procedure was used to collect study sample. Structural Equation Modeling was used to identify relationship between the endogenous and exogenous variables. Finally, the empirically tested psychosocial work environment model will further help in providing a better risk assessment in different industries and enterprises.

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.

Quantitative risk assessment of Cryptosporidium in tap water in Ireland.

PubMed

Cummins, E; Kennedy, R; Cormican, M

2010-01-15

Cryptosporidium species are protozoan parasites associated with gastro-intestinal illness. Following a number of high profile outbreaks worldwide, it has emerged as a parasite of major public health concern. A quantitative Monte Carlo simulation model was developed to evaluate the annual risk of infection from Cryptosporidium in tap water in Ireland. The assessment considers the potential initial contamination levels in raw water, oocyst removal and decontamination events following various process stages, including coagulation/flocculation, sedimentation, filtration and disinfection. A number of scenarios were analysed to represent potential risks from public water supplies, group water schemes and private wells. Where surface water is used additional physical and chemical water treatment is important in terms of reducing the risk to consumers. The simulated annual risk of illness for immunocompetent individuals was below 1 x 10(-4) per year (as set by the US EPA) except under extreme contamination events. The risk for immunocompromised individuals was 2-3 orders of magnitude greater for the scenarios analysed. The model indicates a reduced risk of infection from tap water that has undergone microfiltration, as this treatment is more robust in the event of high contamination loads. The sensitivity analysis highlighted the importance of watershed protection and the importance of adequate coagulation/flocculation in conventional treatment. The frequency of failure of the treatment process is the most important parameter influencing human risk in conventional treatment. The model developed in this study may be useful for local authorities, government agencies and other stakeholders to evaluate the likely risk of infection given some basic input data on source water and treatment processes used. Copyright 2009 Elsevier B.V. All rights reserved.

PREDICTING THE RISKS OF NEUROTOXIC VOLATILE ORGANIC COMPOUNDS BASED ON TARGET TISSUE DOSE.

EPA Science Inventory

Quantitative exposure-dose-response models relate the external exposure of a substance to the dose in the target tissue, and then relate the target tissue dose to production of adverse outcomes. We developed exposure-dose-response models to describe the affects of acute exposure...

A quantitative risk assessment model to evaluate effective border control measures for rabies prevention

PubMed Central

Weng, Hsin-Yi; Wu, Pei-I; Yang, Ping-Cheng; Tsai, Yi-Lun; Chang, Chao-Chin

2009-01-01

Border control is the primary method to prevent rabies emergence. This study developed a quantitative risk model incorporating stochastic processes to evaluate whether border control measures could efficiently prevent rabies introduction through importation of cats and dogs using Taiwan as an example. Both legal importation and illegal smuggling were investigated. The impacts of reduced quarantine and/or waiting period on the risk of rabies introduction were also evaluated. The results showed that Taiwan’s current animal importation policy could effectively prevent rabies introduction through legal importation of cats and dogs. The median risk of a rabid animal to penetrate current border control measures and enter Taiwan was 5.33 × 10−8 (95th percentile: 3.20 × 10−7). However, illegal smuggling may pose Taiwan to the great risk of rabies emergence. Reduction of quarantine and/or waiting period would affect the risk differently, depending on the applied assumptions, such as increased vaccination coverage, enforced custom checking, and/or change in number of legal importations. Although the changes in the estimated risk under the assumed alternatives were not substantial except for completely abolishing quarantine, the consequences of rabies introduction may yet be considered to be significant in a rabies-free area. Therefore, a comprehensive benefit-cost analysis needs to be conducted before recommending these alternative measures. PMID:19822125

Investigating Uncertainty and Sensitivity in Integrated, Multimedia Environmental Models: Tools for FRAMES-3MRA

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

Babendreier, Justin E.; Castleton, Karl J.

2005-08-01

Elucidating uncertainty and sensitivity structures in environmental models can be a difficult task, even for low-order, single-medium constructs driven by a unique set of site-specific data. Quantitative assessment of integrated, multimedia models that simulate hundreds of sites, spanning multiple geographical and ecological regions, will ultimately require a comparative approach using several techniques, coupled with sufficient computational power. The Framework for Risk Analysis in Multimedia Environmental Systems - Multimedia, Multipathway, and Multireceptor Risk Assessment (FRAMES-3MRA) is an important software model being developed by the United States Environmental Protection Agency for use in risk assessment of hazardous waste management facilities. The 3MRAmore » modeling system includes a set of 17 science modules that collectively simulate release, fate and transport, exposure, and risk associated with hazardous contaminants disposed of in land-based waste management units (WMU) .« less

Quantitative microbiological risk assessment in food industry: Theory and practical application.

PubMed

Membré, Jeanne-Marie; Boué, Géraldine

2018-04-01

The objective of this article is to bring scientific background as well as practical hints and tips to guide risk assessors and modelers who want to develop a quantitative Microbiological Risk Assessment (MRA) in an industrial context. MRA aims at determining the public health risk associated with biological hazards in a food. Its implementation in industry enables to compare the efficiency of different risk reduction measures, and more precisely different operational settings, by predicting their effect on the final model output. The first stage in MRA is to clearly define the purpose and scope with stakeholders, risk assessors and modelers. Then, a probabilistic model is developed; this includes schematically three important phases. Firstly, the model structure has to be defined, i.e. the connections between different operational processing steps. An important step in food industry is the thermal processing leading to microbial inactivation. Growth of heat-treated surviving microorganisms and/or post-process contamination during storage phase is also important to take into account. Secondly, mathematical equations are determined to estimate the change of microbial load after each processing step. This phase includes the construction of model inputs by collecting data or eliciting experts. Finally, the model outputs are obtained by simulation procedures, they have to be interpreted and communicated to targeted stakeholders. In this latter phase, tools such as what-if scenarios provide an essential added value. These different MRA phases are illustrated through two examples covering important issues in industry. The first one covers process optimization in a food safety context, the second one covers shelf-life determination in a food quality context. Although both contexts required the same methodology, they do not have the same endpoint: up to the human health in the foie gras case-study illustrating here a safety application, up to the food portion in the brioche case-study illustrating here a quality application. Copyright © 2017 Elsevier Ltd. All rights reserved.

Developing a java android application of KMV-Merton default rate model

NASA Astrophysics Data System (ADS)

Yusof, Norliza Muhamad; Anuar, Aini Hayati; Isa, Norsyaheeda Natasha; Zulkafli, Sharifah Nursyuhada Syed; Sapini, Muhamad Luqman

2017-11-01

This paper presents a developed java android application for KMV-Merton model in predicting the defaut rate of a firm. Predicting default rate is essential in the risk management area as default risk can be immediately transmitted from one entity to another entity. This is the reason default risk is known as a global risk. Although there are several efforts, instruments and methods used to manage the risk, it is said to be insufficient. To the best of our knowledge, there has been limited innovation in developing the default risk mathematical model into a mobile application. Therefore, through this study, default risk is predicted quantitatively using the KMV-Merton model. The KMV-Merton model has been integrated in the form of java program using the Android Studio Software. The developed java android application is tested by predicting the levels of default risk of the three different rated companies. It is found that the levels of default risk are equivalent to the ratings of the respective companies. This shows that the default rate predicted by the KMV-Merton model using the developed java android application can be a significant tool to the risk mangement field. The developed java android application grants users an alternative to predict level of default risk within less procedure.

Quantitative Microbial Risk Assessment Models for Consumption of Raw Vegetables Irrigated with Reclaimed Water

PubMed Central

Hamilton, Andrew J.; Stagnitti, Frank; Premier, Robert; Boland, Anne-Maree; Hale, Glenn

2006-01-01

Quantitative microbial risk assessment models for estimating the annual risk of enteric virus infection associated with consuming raw vegetables that have been overhead irrigated with nondisinfected secondary treated reclaimed water were constructed. We ran models for several different scenarios of crop type, viral concentration in effluent, and time since last irrigation event. The mean annual risk of infection was always less for cucumber than for broccoli, cabbage, or lettuce. Across the various crops, effluent qualities, and viral decay rates considered, the annual risk of infection ranged from 10−3 to 10−1 when reclaimed-water irrigation ceased 1 day before harvest and from 10−9 to 10−3 when it ceased 2 weeks before harvest. Two previously published decay coefficients were used to describe the die-off of viruses in the environment. For all combinations of crop type and effluent quality, application of the more aggressive decay coefficient led to annual risks of infection that satisfied the commonly propounded benchmark of ≤10−4, i.e., one infection or less per 10,000 people per year, providing that 14 days had elapsed since irrigation with reclaimed water. Conversely, this benchmark was not attained for any combination of crop and water quality when this withholding period was 1 day. The lower decay rate conferred markedly less protection, with broccoli and cucumber being the only crops satisfying the 10−4 standard for all water qualities after a 14-day withholding period. Sensitivity analyses on the models revealed that in nearly all cases, variation in the amount of produce consumed had the most significant effect on the total uncertainty surrounding the estimate of annual infection risk. The models presented cover what would generally be considered to be worst-case scenarios: overhead irrigation and consumption of vegetables raw. Practices such as subsurface, furrow, or drip irrigation and postharvest washing/disinfection and food preparation could substantially lower risks and need to be considered in future models, particularly for developed nations where these extra risk reduction measures are more common. PMID:16672468

COLLABORATION ON NHEERL EPIDEMIOLOGY STUDIES

EPA Science Inventory

This task will continue ORD's efforts to develop a biologically plausible, quantitative health risk model for particulate matter (PM) based on epidemiological, toxicological, and mechanistic studies using matched exposure assessments. The NERL, in collaboration with the NHEERL, ...

A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

NASA Astrophysics Data System (ADS)

Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen

2017-07-01

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.

PubMed

Li, Shancang; Tryfonas, Theo; Russell, Gordon; Andriotis, Panagiotis

2016-08-01

Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess the security risks posed by each mobile applications (apps), thus gaining a stronger understanding of any vulnerabilities present. This paper aims at developing a three-layer framework that assesses the potential risks which apps introduce within the Android mobile systems. A Bayesian risk graphical model is proposed to evaluate risk propagation in a layered risk architecture. By integrating static analysis, dynamic analysis, and behavior analysis in a hierarchical framework, the risks and their propagation through each layer are well modeled by the Bayesian risk graph, which can quantitatively analyze risks faced to both apps and mobile systems. The proposed hierarchical Bayesian risk graph model offers a novel way to investigate the security risks in mobile environment and enables users and administrators to evaluate the potential risks. This strategy allows to strengthen both app security as well as the security of the entire system.

Analyses in support of risk-informed natural gas vehicle maintenance facility codes and standards :

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

Ekoto, Isaac W.; Blaylock, Myra L.; LaFleur, Angela Christine

2014-03-01

Safety standards development for maintenance facilities of liquid and compressed gas fueled large-scale vehicles is required to ensure proper facility design and operation envelopes. Standard development organizations are utilizing risk-informed concepts to develop natural gas vehicle (NGV) codes and standards so that maintenance facilities meet acceptable risk levels. The present report summarizes Phase I work for existing NGV repair facility code requirements and highlights inconsistencies that need quantitative analysis into their effectiveness. A Hazardous and Operability study was performed to identify key scenarios of interest. Finally, scenario analyses were performed using detailed simulations and modeling to estimate the overpressure hazardsmore » from HAZOP defined scenarios. The results from Phase I will be used to identify significant risk contributors at NGV maintenance facilities, and are expected to form the basis for follow-on quantitative risk analysis work to address specific code requirements and identify effective accident prevention and mitigation strategies.« less

Integrated Research on the Development of Global Climate Risk Management Strategies - Framework and Initial Results of the Research Project ICA-RUS

NASA Astrophysics Data System (ADS)

Emori, Seita; Takahashi, Kiyoshi; Yamagata, Yoshiki; Oki, Taikan; Mori, Shunsuke; Fujigaki, Yuko

2013-04-01

With the aim of proposing strategies of global climate risk management, we have launched a five-year research project called ICA-RUS (Integrated Climate Assessment - Risks, Uncertainties and Society). In this project with the phrase "risk management" in its title, we aspire for a comprehensive assessment of climate change risks, explicit consideration of uncertainties, utilization of best available information, and consideration of every possible conditions and options. We also regard the problem as one of decision-making at the human level, which involves social value judgments and adapts to future changes in circumstances. The ICA-RUS project consists of the following five themes: 1) Synthesis of global climate risk management strategies, 2) Optimization of land, water and ecosystem uses for climate risk management, 3) Identification and analysis of critical climate risks, 4) Evaluation of climate risk management options under technological, social and economic uncertainties and 5) Interactions between scientific and social rationalities in climate risk management (see also: http://www.nies.go.jp/ica-rus/en/). For the integration of quantitative knowledge of climate change risks and responses, we apply a tool named AIM/Impact [Policy], which consists of an energy-economic model, a simplified climate model and impact projection modules. At the same time, in order to make use of qualitative knowledge as well, we hold monthly project meetings for the discussion of risk management strategies and publish annual reports based on the quantitative and qualitative information. To enhance the comprehensiveness of the analyses, we maintain an inventory of risks and risk management options. The inventory is revised iteratively through interactive meetings with stakeholders such as policymakers, government officials and industrial representatives.

Quantitative coronary plaque analysis predicts high-risk plaque morphology on coronary computed tomography angiography: results from the ROMICAT II trial.

PubMed

Liu, Ting; Maurovich-Horvat, Pál; Mayrhofer, Thomas; Puchner, Stefan B; Lu, Michael T; Ghemigian, Khristine; Kitslaar, Pieter H; Broersen, Alexander; Pursnani, Amit; Hoffmann, Udo; Ferencik, Maros

2018-02-01

Semi-automated software can provide quantitative assessment of atherosclerotic plaques on coronary CT angiography (CTA). The relationship between established qualitative high-risk plaque features and quantitative plaque measurements has not been studied. We analyzed the association between quantitative plaque measurements and qualitative high-risk plaque features on coronary CTA. We included 260 patients with plaque who underwent coronary CTA in the Rule Out Myocardial Infarction/Ischemia Using Computer Assisted Tomography (ROMICAT) II trial. Quantitative plaque assessment and qualitative plaque characterization were performed on a per coronary segment basis. Quantitative coronary plaque measurements included plaque volume, plaque burden, remodeling index, and diameter stenosis. In qualitative analysis, high-risk plaque was present if positive remodeling, low CT attenuation plaque, napkin-ring sign or spotty calcium were detected. Univariable and multivariable logistic regression analyses were performed to assess the association between quantitative and qualitative high-risk plaque assessment. Among 888 segments with coronary plaque, high-risk plaque was present in 391 (44.0%) segments by qualitative analysis. In quantitative analysis, segments with high-risk plaque had higher total plaque volume, low CT attenuation plaque volume, plaque burden and remodeling index. Quantitatively assessed low CT attenuation plaque volume (odds ratio 1.12 per 1 mm 3 , 95% CI 1.04-1.21), positive remodeling (odds ratio 1.25 per 0.1, 95% CI 1.10-1.41) and plaque burden (odds ratio 1.53 per 0.1, 95% CI 1.08-2.16) were associated with high-risk plaque. Quantitative coronary plaque characteristics (low CT attenuation plaque volume, positive remodeling and plaque burden) measured by semi-automated software correlated with qualitative assessment of high-risk plaque features.

Estimating exposures in the asphalt industry for an international epidemiological cohort study of cancer risk.

PubMed

Burstyn, Igor; Boffetta, Paolo; Kauppinen, Timo; Heikkilä, Pirjo; Svane, Ole; Partanen, Timo; Stücker, Isabelle; Frentzel-Beyme, Rainer; Ahrens, Wolfgang; Merzenich, Hiltrud; Heederik, Dick; Hooiveld, Mariëtte; Langård, Sverre; Randem, Britt G; Järvholm, Bengt; Bergdahl, Ingvar; Shaham, Judith; Ribak, Joseph; Kromhout, Hans

2003-01-01

An exposure matrix (EM) for known and suspected carcinogens was required for a multicenter international cohort study of cancer risk and bitumen among asphalt workers. Production characteristics in companies enrolled in the study were ascertained through use of a company questionnaire (CQ). Exposures to coal tar, bitumen fume, organic vapor, polycyclic aromatic hydrocarbons, diesel fume, silica, and asbestos were assessed semi-quantitatively using information from CQs, expert judgment, and statistical models. Exposures of road paving workers to bitumen fume, organic vapor, and benzo(a)pyrene were estimated quantitatively by applying regression models, based on monitoring data, to exposure scenarios identified by the CQs. Exposures estimates were derived for 217 companies enrolled in the cohort, plus the Swedish asphalt paving industry in general. Most companies were engaged in road paving and asphalt mixing, but some also participated in general construction and roofing. Coal tar use was most common in Denmark and The Netherlands, but the practice is now obsolete. Quantitative estimates of exposure to bitumen fume, organic vapor, and benzo(a)pyrene for pavers, and semi-quantitative estimates of exposure to these agents among all subjects were strongly correlated. Semi-quantitative estimates of exposure to bitumen fume and coal tar exposures were only moderately correlated. EM assessed non-monotonic historical decrease in exposures to all agents assessed except silica and diesel exhaust. We produced a data-driven EM using methodology that can be adapted for other multicenter studies. Copyright 2003 Wiley-Liss, Inc.

Human ex-vivo action potential model for pro-arrhythmia risk assessment.

PubMed

Page, Guy; Ratchada, Phachareeya; Miron, Yannick; Steiner, Guido; Ghetti, Andre; Miller, Paul E; Reynolds, Jack A; Wang, Ken; Greiter-Wilke, Andrea; Polonchuk, Liudmila; Traebert, Martin; Gintant, Gary A; Abi-Gerges, Najah

2016-01-01

While current S7B/E14 guidelines have succeeded in protecting patients from QT-prolonging drugs, the absence of a predictive paradigm identifying pro-arrhythmic risks has limited the development of valuable drug programs. We investigated if a human ex-vivo action potential (AP)-based model could provide a more predictive approach for assessing pro-arrhythmic risk in man. Human ventricular trabeculae from ethically consented organ donors were used to evaluate the effects of dofetilide, d,l-sotalol, quinidine, paracetamol and verapamil on AP duration (APD) and recognized pro-arrhythmia predictors (short-term variability of APD at 90% repolarization (STV(APD90)), triangulation (ADP90-APD30) and incidence of early afterdepolarizations at 1 and 2Hz to quantitatively identify the pro-arrhythmic risk. Each drug was blinded and tested separately with 3 concentrations in triplicate trabeculae from 5 hearts, with one vehicle time control per heart. Electrophysiological stability of the model was not affected by sequential applications of vehicle (0.1% dimethyl sulfoxide). Paracetamol and verapamil did not significantly alter anyone of the AP parameters and were classified as devoid of pro-arrhythmic risk. Dofetilide, d,l-sotalol and quinidine exhibited an increase in the manifestation of pro-arrhythmia markers. The model provided quantitative and actionable activity flags and the relatively low total variability in tissue response allowed for the identification of pro-arrhythmic signals. Power analysis indicated that a total of 6 trabeculae derived from 2 hearts are sufficient to identify drug-induced pro-arrhythmia. Thus, the human ex-vivo AP-based model provides an integrative translational assay assisting in shaping clinical development plans that could be used in conjunction with the new CiPA-proposed approach. Copyright © 2016 Elsevier Inc. All rights reserved.

Human ex-vivo action potential model for pro-arrhythmia risk assessment

PubMed Central

Page, Guy; Ratchada, Phachareeya; Miron, Yannick; Steiner, Guido; Ghetti, Andre; Miller, Paul E; Reynolds, Jack A; Wang, Ken; Greiter-Wilke, Andrea; Polonchuk, Liudmila; Traebert, Martin; Gintant, Gary A; Abi-Gerges, Najah

2016-01-01

While current S7B/E14 guidelines have succeeded in protecting patients from QT-prolonging drugs, the absence of a predictive paradigm identifying pro-arrhythmic risks has limited the development of valuable drug programs. We investigated if a human ex-vivo action potential (AP)-based model could provide a more predictive approach for assessing pro-arrhythmic risk in man. Human ventricular trabeculae from ethically consented organ donors were used to evaluate the effects of dofetilide, d,l-sotalol, quinidine, paracetamol and verapamil on AP duration (APD) and recognized pro-arrhythmia predictors (short-term variability of APD at 90% repolarization (STV(APD90)), triangulation (ADP90-APD30) and incidence of early afterdepolarizations at 1 and 2 Hz to quantitatively identify the pro-arrhythmic risk. Each drug was blinded and tested separately with 3 concentrations in triplicate trabeculae from 5 hearts, with one vehicle time control per heart. Electrophysiological stability of the model was not affected by sequential applications of vehicle (0.1% dimethyl sulfoxide). Paracetamol and verapamil did not significantly alter anyone of the AP parameters and were classified as devoid of pro-arrhythmic risk. Dofetilide, d,l-sotalol and quinidine exhibited an increase in the manifestation of pro-arrhythmia markers. The model provided quantitative and actionable activity flags and the relatively low total variability in tissue response allowed for the identification of pro-arrhythmic signals. Power analysis indicated that a total of 6 trabeculae derived from 2 hearts are sufficient to identify drug-induced pro-arrhythmia. Thus, the human ex-vivo AP-based model provides an integrative translational assay assisting in shaping clinical development plans that could be used in conjunction with the new CiPA-proposed approach. PMID:27235787

Quantitative microbial risk assessment for Escherichia coli O157:H7, salmonella, and Listeria monocytogenes in leafy green vegetables consumed at salad bars.

PubMed

Franz, E; Tromp, S O; Rijgersberg, H; van der Fels-Klerx, H J

2010-02-01

Fresh vegetables are increasingly recognized as a source of foodborne outbreaks in many parts of the world. The purpose of this study was to conduct a quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes infection from consumption of leafy green vegetables in salad from salad bars in The Netherlands. Pathogen growth was modeled in Aladin (Agro Logistics Analysis and Design Instrument) using time-temperature profiles in the chilled supply chain and one particular restaurant with a salad bar. A second-order Monte Carlo risk assessment model was constructed (using @Risk) to estimate the public health effects. The temperature in the studied cold chain was well controlled below 5 degrees C. Growth of E. coli O157:H7 and Salmonella was minimal (17 and 15%, respectively). Growth of L. monocytogenes was considerably greater (194%). Based on first-order Monte Carlo simulations, the average number of cases per year in The Netherlands associated the consumption leafy greens in salads from salad bars was 166, 187, and 0.3 for E. coli O157:H7, Salmonella, and L. monocytogenes, respectively. The ranges of the average number of annual cases as estimated by second-order Monte Carlo simulation (with prevalence and number of visitors as uncertain variables) were 42 to 551 for E. coli O157:H7, 81 to 281 for Salmonella, and 0.1 to 0.9 for L. monocytogenes. This study included an integration of modeling pathogen growth in the supply chain of fresh leafy vegetables destined for restaurant salad bars using software designed to model and design logistics and modeling the public health effects using probabilistic risk assessment software.

76 FR 19311 - Update of the 2003 Interagency Quantitative Assessment of the Relative Risk to Public Health From...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-07

... the 2003 Interagency Quantitative Assessment of the Relative Risk to Public Health From Foodborne... quantitative targets established in ``Healthy People 2010.'' In 2005, FoodNet data showed 0.30 L. monocytogenes... 4). In 2003, FDA and FSIS published a quantitative assessment of the relative risk to public health...

Effects of a 20 year rain event: a quantitative microbial risk assessment of a case of contaminated bathing water in Copenhagen, Denmark.

PubMed

Andersen, S T; Erichsen, A C; Mark, O; Albrechtsen, H-J

2013-12-01

Quantitative microbial risk assessments (QMRAs) often lack data on water quality leading to great uncertainty in the QMRA because of the many assumptions. The quantity of waste water contamination was estimated and included in a QMRA on an extreme rain event leading to combined sewer overflow (CSO) to bathing water where an ironman competition later took place. Two dynamic models, (1) a drainage model and (2) a 3D hydrodynamic model, estimated the dilution of waste water from source to recipient. The drainage model estimated that 2.6% of waste water was left in the system before CSO and the hydrodynamic model estimated that 4.8% of the recipient bathing water came from the CSO, so on average there was 0.13% of waste water in the bathing water during the ironman competition. The total estimated incidence rate from a conservative estimate of the pathogenic load of five reference pathogens was 42%, comparable to 55% in an epidemiological study of the case. The combination of applying dynamic models and exposure data led to an improved QMRA that included an estimate of the dilution factor. This approach has not been described previously.

Assessing pesticide risks to threatened and endangered species using population models: Findings and recommendations from a CropLife America Science Forum.

PubMed

Forbes, V E; Brain, R; Edwards, D; Galic, N; Hall, T; Honegger, J; Meyer, C; Moore, D R J; Nacci, D; Pastorok, R; Preuss, T G; Railsback, S F; Salice, C; Sibly, R M; Tenhumberg, B; Thorbek, P; Wang, M

2015-07-01

This brief communication reports on the main findings and recommendations from the 2014 Science Forum organized by CropLife America. The aim of the Forum was to gain a better understanding of the current status of population models and how they could be used in ecological risk assessments for threatened and endangered species potentially exposed to pesticides in the United States. The Forum panelists' recommendations are intended to assist the relevant government agencies with implementation of population modeling in future endangered species risk assessments for pesticides. The Forum included keynote presentations that provided an overview of current practices, highlighted the findings of a recent National Academy of Sciences report and its implications, reviewed the main categories of existing population models and the types of risk expressions that can be produced as model outputs, and provided examples of how population models are currently being used in different legislative contexts. The panel concluded that models developed for listed species assessments should provide quantitative risk estimates, incorporate realistic variability in environmental and demographic factors, integrate complex patterns of exposure and effects, and use baseline conditions that include present factors that have caused the species to be listed (e.g., habitat loss, invasive species) or have resulted in positive management action. Furthermore, the panel advocates for the formation of a multipartite advisory committee to provide best available knowledge and guidance related to model implementation and use, to address such needs as more systematic collection, digitization, and dissemination of data for listed species; consideration of the newest developments in good modeling practice; comprehensive review of existing population models and their applicability for listed species assessments; and development of case studies using a few well-tested models for particular species to demonstrate proof of concept. To advance our common goals, the panel recommends the following as important areas for further research and development: quantitative analysis of the causes of species listings to guide model development; systematic assessment of the relative role of toxicity versus other factors in driving pesticide risk; additional study of how interactions between density dependence and pesticides influence risk; and development of pragmatic approaches to assessing indirect effects of pesticides on listed species. © 2015 SETAC.

A model of pathways to artificial superintelligence catastrophe for risk and decision analysis

NASA Astrophysics Data System (ADS)

Barrett, Anthony M.; Baum, Seth D.

2017-03-01

An artificial superintelligence (ASI) is an artificial intelligence that is significantly more intelligent than humans in all respects. Whilst ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modelling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, development and management. Model structure is derived from published literature on ASI risk. The model offers a foundation for rigorous quantitative evaluation and decision-making on the long-term risk of ASI catastrophe.

A quantitative risk model for early lifecycle decision making

NASA Technical Reports Server (NTRS)

Feather, M. S.; Cornford, S. L.; Dunphy, J.; Hicks, K.

2002-01-01

Decisions made in the earliest phases of system development have the most leverage to influence the success of the entire development effort, and yet must be made when information is incomplete and uncertain. We have developed a scalable cost-benefit model to support this critical phase of early-lifecycle decision-making.

Does Psychosocial Work Environment Factors Predict Stress and Mean Arterial Pressure in the Malaysian Industry Workers?

PubMed Central

Isha, Ahmad Shahrul Nizam; Sabir, Asrar Ahmed; Ghazali, Zulkipli; Nübling, Matthias

2018-01-01

Psychosocial risks are considered as a burning issue in the Asia-Pacific region. The aim of this study was to investigate the impact of psychosocial work environment factors on health of petrochemical industry workers of Malaysia. In lieu to job demands-resources theory, significant positive associations were found between quantitative demands, work-family conflict, and job insecurity with stress, while a significant negative association of role clarity as a resource factor with stress was detected. We also found that quantitative demands were significantly associated with the mean arterial pressure (MAP). Multistage sampling procedure was used to collect study sample. Structural Equation Modeling was used to identify relationship between the endogenous and exogenous variables. Finally, the empirically tested psychosocial work environment model will further help in providing a better risk assessment in different industries and enterprises. PMID:29568773

Quantitative assessment model for gastric cancer screening

PubMed Central

Chen, Kun; Yu, Wei-Ping; Song, Liang; Zhu, Yi-Min

2005-01-01

AIM: To set up a mathematic model for gastric cancer screening and to evaluate its function in mass screening for gastric cancer. METHODS: A case control study was carried on in 66 patients and 198 normal people, then the risk and protective factors of gastric cancer were determined, including heavy manual work, foods such as small yellow-fin tuna, dried small shrimps, squills, crabs, mothers suffering from gastric diseases, spouse alive, use of refrigerators and hot food, etc. According to some principles and methods of probability and fuzzy mathematics, a quantitative assessment model was established as follows: first, we selected some factors significant in statistics, and calculated weight coefficient for each one by two different methods; second, population space was divided into gastric cancer fuzzy subset and non gastric cancer fuzzy subset, then a mathematic model for each subset was established, we got a mathematic expression of attribute degree (AD). RESULTS: Based on the data of 63 patients and 693 normal people, AD of each subject was calculated. Considering the sensitivity and specificity, the thresholds of AD values calculated were configured with 0.20 and 0.17, respectively. According to these thresholds, the sensitivity and specificity of the quantitative model were about 69% and 63%. Moreover, statistical test showed that the identification outcomes of these two different calculation methods were identical (P>0.05). CONCLUSION: The validity of this method is satisfactory. It is convenient, feasible, economic and can be used to determine individual and population risks of gastric cancer. PMID:15655813

From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

PubMed Central

Zhu, Hao

2017-01-01

Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

Quantitative Gait Markers and Incident Fall Risk in Older Adults

PubMed Central

Holtzer, Roee; Lipton, Richard B.; Wang, Cuiling

2009-01-01

Background Identifying quantitative gait markers of falls in older adults may improve diagnostic assessments and suggest novel intervention targets. Methods We studied 597 adults aged 70 and older (mean age 80.5 years, 62% women) enrolled in an aging study who received quantitative gait assessments at baseline. Association of speed and six other gait markers (cadence, stride length, swing, double support, stride length variability, and swing time variability) with incident fall rate was studied using generalized estimation equation procedures adjusted for age, sex, education, falls, chronic illnesses, medications, cognition, disability as well as traditional clinical tests of gait and balance. Results Over a mean follow-up period of 20 months, 226 (38%) of the 597 participants fell. Mean fall rate was 0.44 per person-year. Slower gait speed (risk ratio [RR] per 10 cm/s decrease 1.069, 95% confidence interval [CI] 1.001–1.142) was associated with higher risk of falls in the fully adjusted models. Among six other markers, worse performance on swing (RR 1.406, 95% CI 1.027–1.926), double-support phase (RR 1.165, 95% CI 1.026–1.321), swing time variability (RR 1.007, 95% CI 1.004–1.010), and stride length variability (RR 1.076, 95% CI 1.030–1.111) predicted fall risk. The associations remained significant even after accounting for cognitive impairment and disability. Conclusions Quantitative gait markers are independent predictors of falls in older adults. Gait speed and other markers, especially variability, should be further studied to improve current fall risk assessments and to develop new interventions. PMID:19349593

Revised Risk Priority Number in Failure Mode and Effects Analysis Model from the Perspective of Healthcare System

PubMed Central

Rezaei, Fatemeh; Yarmohammadian, Mohmmad H.; Haghshenas, Abbas; Fallah, Ali; Ferdosi, Masoud

2018-01-01

Background: Methodology of Failure Mode and Effects Analysis (FMEA) is known as an important risk assessment tool and accreditation requirement by many organizations. For prioritizing failures, the index of “risk priority number (RPN)” is used, especially for its ease and subjective evaluations of occurrence, the severity and the detectability of each failure. In this study, we have tried to apply FMEA model more compatible with health-care systems by redefining RPN index to be closer to reality. Methods: We used a quantitative and qualitative approach in this research. In the qualitative domain, focused groups discussion was used to collect data. A quantitative approach was used to calculate RPN score. Results: We have studied patient's journey in surgery ward from holding area to the operating room. The highest priority failures determined based on (1) defining inclusion criteria as severity of incident (clinical effect, claim consequence, waste of time and financial loss), occurrence of incident (time - unit occurrence and degree of exposure to risk) and preventability (degree of preventability and defensive barriers) then, (2) risks priority criteria quantified by using RPN index (361 for the highest rate failure). The ability of improved RPN scores reassessed by root cause analysis showed some variations. Conclusions: We concluded that standard criteria should be developed inconsistent with clinical linguistic and special scientific fields. Therefore, cooperation and partnership of technical and clinical groups are necessary to modify these models. PMID:29441184

[Health risk assessment of coke oven PAHs emissions].

PubMed

Bo, Xin; Wang, Gang; Wen, Rou; Zhao, Chun-Li; Wu, Tie; Li, Shi-Bei

2014-07-01

Polycyclic aromatic hydrocarbons (PAHs) produced by coke oven are with strong toxicity and carcinogenicity. Taken typical coke oven of iron and steel enterprises as the case study, the dispersion and migration of 13 kinds of PAHs emitted from coke oven were analyzed using AERMOD dispersion model, the carcinogenic and non-carcinogenic risks at the receptors within the modeling domain were evaluated using BREEZE Risk Analyst and the Human Health Risk Assessment Protocol for Hazardous Waste Combustion (HHRAP) was followed, the health risks caused by PAHs emission from coke oven were quantitatively evaluated. The results indicated that attention should be paid to the non-carcinogenic risk of naphthalene emission (the maximum value was 0.97). The carcinogenic risks of each single pollutant were all below 1.0E-06, while the maximum value of total carcinogenic risk was 2.65E-06, which may have some influence on the health of local residents.

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models.

PubMed

Allen, R J; Rieger, T R; Musante, C J

2016-03-01

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed "virtual patients." In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations.

A new methodology for dynamic modelling of health risks arising from wastewater influenced urban flooding

NASA Astrophysics Data System (ADS)

Jørgensen, Claus; Mark, Ole; Djordjevic, Slobodan; Hammond, Michael; Khan, David M.; Erichsen, Anders; Dorrit Enevoldsen, Ann; Heinicke, Gerald; Helwigh, Birgitte

2015-04-01

Indroduction Urban flooding due to rainfall exceeding the design capacity of drainage systems is a global problem and it has significant economic and social consequences. While the cost of the direct flood damages of urban flooding is well understood, the indirect damages, like the water borne diseases is in general still poorly understood. Climate changes are expected to increase the frequency of urban flooding in many countries which is likely to increase water borne diseases. Diarrheal diseases are most prevalent in developing countries, where poor sanitation, poor drinking water and poor surface water quality causes a high disease burden and mortality, especially during floods. The level of water borne diarrhea in countries with well-developed water and waste water infrastructure has been reduced to an acceptable level, and the population in general do not consider waste water as being a health risk. Hence, exposure to wastewater influenced urban flood water still has the potential to cause transmission of diarrheal diseases. When managing urban flooding and planning urban climate change adaptations, health risks are rarely taken into consideration. This paper outlines a novel methodology for linking dynamic urban flood modelling with Quantitative Microbial Risk Assessment (QMRA). This provides a unique possibility for understanding the interaction between urban flooding and the health risks caused by direct human contact with flood water and provides an option for reducing the burden of disease in the population through the use of intelligent urban flood risk management. Methodology We have linked hydrodynamic urban flood modelling with quantitative microbial risk assessment (QMRA) to determine the risk of infection caused by exposure to wastewater influenced urban flood water. The deterministic model MIKE Flood, which integrates the sewer network model in MIKE Urban and the 2D surface model MIKE21, was used to calculate the concentration of pathogens in the flood water, based on either measured waste water pathogen concentrations or on assumptions regarding the prevalence of infections in the population. The exposure (dosage) to pathogens was estimated by multiplying the concentration with literature values for the ingestion of water for different exposure groups (e.g. children, adults). The probability of infection was determined by applying dose response relations and MonteCarlo simulation. The methodology is demonstrated on two cases, i.e one case from a developing country with poor sanitation and one case from a developed country, where climate adaptation is the main issue: The risk of cholera in the City of Dhaka, Bangladesh during a flood event 2004, and the risk of bacterial and viral infections of during a flood event in Copenhagen, Denmark in 2011. Results PIC The historical flood events in Dhaka (2004) and Copenhagen (2011) were successfully modelled. The urban flood model was successfully coupled to QMRA. An example of the results of the quantitative microbial risk assessment given as the average estimated risk of cholera infection for children below 5 years living in slum areas in Dhaka is shown in the figure. Similarly, the risk of infection during the flood event in Copenhagen will be presented in the article. Conclusions We have developed a methodology for the dynamic modeling of the risk of infection during waste water influenced urban flooding. The outcome of the modelling exercise indicates that direct contact with polluted flood water is a likely route of transmission of cholera in Dhaka, and bacterial and viral infectious diseases in Copenhagen. It demonstrates the applicability and the potential for linking urban flood models with QMRA in order to identify interventions to reduce the burden of disease on the population in Dhaka City and Copenhagen.

Probabilistic risk assessment of the Space Shuttle. Phase 3: A study of the potential of losing the vehicle during nominal operation, volume 1

NASA Technical Reports Server (NTRS)

Fragola, Joseph R.; Maggio, Gaspare; Frank, Michael V.; Gerez, Luis; Mcfadden, Richard H.; Collins, Erin P.; Ballesio, Jorge; Appignani, Peter L.; Karns, James J.

1995-01-01

This document is the Executive Summary of a technical report on a probabilistic risk assessment (PRA) of the Space Shuttle vehicle performed under the sponsorship of the Office of Space Flight of the US National Aeronautics and Space Administration. It briefly summarizes the methodology and results of the Shuttle PRA. The primary objective of this project was to support management and engineering decision-making with respect to the Shuttle program by producing (1) a quantitative probabilistic risk model of the Space Shuttle during flight, (2) a quantitative assessment of in-flight safety risk, (3) an identification and prioritization of the design and operations that principally contribute to in-flight safety risk, and (4) a mechanism for risk-based evaluation proposed modifications to the Shuttle System. Secondary objectives were to provide a vehicle for introducing and transferring PRA technology to the NASA community, and to demonstrate the value of PRA by applying it beneficially to a real program of great international importance.

Designing automation for human use: empirical studies and quantitative models.

PubMed

Parasuraman, R

2000-07-01

An emerging knowledge base of human performance research can provide guidelines for designing automation that can be used effectively by human operators of complex systems. Which functions should be automated and to what extent in a given system? A model for types and levels of automation that provides a framework and an objective basis for making such choices is described. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design when using the model. Four human performance areas are considered--mental workload, situation awareness, complacency and skill degradation. Secondary evaluative criteria include such factors as automation reliability, the risks of decision/action consequences and the ease of systems integration. In addition to this qualitative approach, quantitative models can inform design. Several computational and formal models of human interaction with automation that have been proposed by various researchers are reviewed. An important future research need is the integration of qualitative and quantitative approaches. Application of these models provides an objective basis for designing automation for effective human use.

Multifactorial disease risk calculator: Risk prediction for multifactorial disease pedigrees.

PubMed

Campbell, Desmond D; Li, Yiming; Sham, Pak C

2018-03-01

Construction of multifactorial disease models from epidemiological findings and their application to disease pedigrees for risk prediction is nontrivial for all but the simplest of cases. Multifactorial Disease Risk Calculator is a web tool facilitating this. It provides a user-friendly interface, extending a reported methodology based on a liability-threshold model. Multifactorial disease models incorporating all the following features in combination are handled: quantitative risk factors (including polygenic scores), categorical risk factors (including major genetic risk loci), stratified age of onset curves, and the partition of the population variance in disease liability into genetic, shared, and unique environment effects. It allows the application of such models to disease pedigrees. Pedigree-related outputs are (i) individual disease risk for pedigree members, (ii) n year risk for unaffected pedigree members, and (iii) the disease pedigree's joint liability distribution. Risk prediction for each pedigree member is based on using the constructed disease model to appropriately weigh evidence on disease risk available from personal attributes and family history. Evidence is used to construct the disease pedigree's joint liability distribution. From this, lifetime and n year risk can be predicted. Example disease models and pedigrees are provided at the website and are used in accompanying tutorials to illustrate the features available. The website is built on an R package which provides the functionality for pedigree validation, disease model construction, and risk prediction. Website: http://grass.cgs.hku.hk:3838/mdrc/current. © 2017 WILEY PERIODICALS, INC.

Correlation of genetic risk and messenger RNA expression in a Th17/IL23 pathway analysis in inflammatory bowel disease.

PubMed

Fransen, Karin; van Sommeren, Suzanne; Westra, Harm-Jan; Veenstra, Monique; Lamberts, Letitia E; Modderman, Rutger; Dijkstra, Gerard; Fu, Jingyuan; Wijmenga, Cisca; Franke, Lude; Weersma, Rinse K; van Diemen, Cleo C

2014-05-01

The Th17/IL23 pathway has both genetically and biologically been implicated in the pathogenesis of the inflammatory bowel diseases (IBD), Crohn's disease, and ulcerative colitis. So far, it is unknown whether and how associated risk variants affect expression of the genes encoding for Th17/IL23 pathway proteins. Ten IBD-associated SNPs residing near Th17/IL23 genes were used to construct a genetic risk model in 753 Dutch IBD cases and 1045 controls. In an independent cohort of 40 Crohn's disease, 40 ulcerative colitis, and 40 controls, the genetic risk load and presence of IBD were correlated to quantitative PCR-generated messenger RNA (mRNA) expression of 9 representative Th17/IL23 genes in both unstimulated and PMA/CaLo stimulated peripheral blood mononuclear cells. In 1240 individuals with various immunological diseases with whole genome genotype and mRNA-expression data, we also assessed correlation between genetic risk load and differential mRNA expression and sought for SNPs affecting expression of all currently known Th17/IL23 pathway genes (cis-expression quantitative trait locus). The presence of IBD, but not the genetic risk load, was correlated to differential mRNA expression for IL6 in unstimulated peripheral blood mononuclear cells and to IL23A and RORC in response to stimulation. The cis-expression quantitative trait locus analysis showed little evidence for correlation between genetic risk load and mRNA expression of Th17/IL23 genes, because we identified for only 2 of 22 Th17/IL23 genes a cis-expression quantitative trait locus single nucleotide polymorphism that is also associated to IBD (STAT3 and CCR6). Our results suggest that only the presence of IBD and not the genetic risk load alters mRNA expression levels of IBD-associated Th17/IL23 genes.

Do psychosocial work conditions predict risk of disability pensioning? An analysis of register-based outcomes using pooled data on 40,554 observations.

PubMed

Clausen, Thomas; Burr, Hermann; Borg, Vilhelm

2014-06-01

To investigate whether high psychosocial job demands (quantitative demands and work pace) and low psychosocial job resources (influence at work and quality of leadership) predicted risk of disability pensioning among employees in four occupational groups--employees working with customers, employees working with clients, office workers and manual workers--in line with the propositions of the Job Demands-Resources (JD-R) model. Survey data from 40,554 individuals were fitted to the DREAM register containing information on payments of disability pension. Using multi-adjusted Cox regression, observations were followed in the DREAM-register to assess risk of disability pensioning. Average follow-up time was 5.9 years (SD=3.0). Low levels of influence at work predicted an increased risk of disability pensioning and medium levels of quantitative demands predicted a decreased risk of disability pensioning in the study population. We found significant interaction effects between job demands and job resources as combinations low quality of leadership and high job demands predicted the highest rate of disability pensioning. Further analyses showed some, but no statistically significant, differences between the four occupational groups in the associations between job demands, job resources and risk of disability pensioning. The study showed that psychosocial job demands and job resources predicted risk of disability pensioning. The direction of some of the observed associations countered the expectations of the JD-R model and the findings of the present study therefore imply that associations between job demands, job resources and adverse labour market outcomes are more complex than conceptualised in the JD-R model. © 2014 the Nordic Societies of Public Health.

Essentiality, toxicity, and uncertainty in the risk assessment of manganese.

PubMed

Boyes, William K

2010-01-01

Risk assessments of manganese by inhalation or oral routes of exposure typically acknowledge the duality of manganese as an essential element at low doses and a toxic metal at high doses. Previously, however, risk assessors were unable to describe manganese pharmacokinetics quantitatively across dose levels and routes of exposure, to account for mass balance, and to incorporate this information into a quantitative risk assessment. In addition, the prior risk assessment of inhaled manganese conducted by the U.S. Environmental Protection Agency (EPA) identified a number of specific factors that contributed to uncertainty in the risk assessment. In response to a petition regarding the use of a fuel additive containing manganese, methylcyclopentadienyl manganese tricarbonyl (MMT), the U.S. EPA developed a test rule under the U.S. Clean Air Act that required, among other things, the generation of pharmacokinetic information. This information was intended not only to aid in the design of health outcome studies, but also to help address uncertainties in the risk assessment of manganese. To date, the work conducted in response to the test rule has yielded substantial pharmacokinetic data. This information will enable the generation of physiologically based pharmacokinetic (PBPK) models capable of making quantitative predictions of tissue manganese concentrations following inhalation and oral exposure, across dose levels, and accounting for factors such as duration of exposure, different species of manganese, and changes of age, gender, and reproductive status. The work accomplished in response to the test rule, in combination with other scientific evidence, will enable future manganese risk assessments to consider tissue dosimetry more comprehensively than was previously possible.

Radiation and cancer risk: a continuing challenge for epidemiologists

PubMed Central

2011-01-01

This paper provides a perspective on epidemiological research on radiation and cancer, a field that has evolved over its six decade history. The review covers the current framework for assessing radiation risk and persistent questions about the details of these risks: is there a threshold and more generally, what is the shape of the dose-response relationship? How do risks vary over time and with age? What factors modify the risk of radiation? The example of radon progeny and lung cancer is considered as a case study, illustrating the modeling of epidemiological data to derive quantitative models and the coherence of the epidemiological and biological evidence. Finally, the manuscript considers the need for ongoing research, even in the face of research over a 60-year span. PMID:21489214

Quantitative microbial risk assessment to estimate the health risk from exposure to noroviruses in polluted surface water in South Africa.

PubMed

Van Abel, Nicole; Mans, Janet; Taylor, Maureen B

2017-10-01

This study assessed the risks posed by noroviruses (NoVs) in surface water used for drinking, domestic, and recreational purposes in South Africa (SA), using a quantitative microbial risk assessment (QMRA) methodology that took a probabilistic approach coupling an exposure assessment with four dose-response models to account for uncertainty. Water samples from three rivers were found to be contaminated with NoV GI (80-1,900 gc/L) and GII (420-9,760 gc/L) leading to risk estimates that were lower for GI than GII. The volume of water consumed and the probabilities of infection were lower for domestic (2.91 × 10 -8 to 5.19 × 10 -1 ) than drinking water exposures (1.04 × 10 -5 to 7.24 × 10 -1 ). The annual probabilities of illness varied depending on the type of recreational water exposure with boating (3.91 × 10 -6 to 5.43 × 10 -1 ) and swimming (6.20 × 10 -6 to 6.42 × 10 -1 ) being slightly greater than playing next to/in the river (5.30 × 10 -7 to 5.48 × 10 -1 ). The QMRA was sensitive to the choice of dose-response model. The risk of NoV infection or illness from contaminated surface water is extremely high in SA, especially for lower socioeconomic individuals, but is similar to reported risks from limited international studies.

Evaluation of Historical and Projected Agricultural Climate Risk Over the Continental US

NASA Astrophysics Data System (ADS)

Zhu, X.; Troy, T. J.; Devineni, N.

2016-12-01

Food demands are rising due to an increasing population with changing food preferences, which places pressure on agricultural systems. In addition, in the past decade climate extremes have highlighted the vulnerability of our agricultural production to climate variability. Quantitative analyses in the climate-agriculture research field have been performed in many studies. However, climate risk still remains difficult to evaluate at large scales yet shows great potential of help us better understand historical climate change impacts and evaluate the future risk given climate projections. In this study, we developed a framework to evaluate climate risk quantitatively by applying statistical methods such as Bayesian regression, distribution fitting, and Monte Carlo simulation. We applied the framework over different climate regions in the continental US both historically and for modeled climate projections. The relative importance of any major growing season climate index, such as maximum dry period or heavy precipitation, was evaluated to determine what climate indices play a role in affecting crop yields. The statistical modeling framework was applied using county yields, with irrigated and rainfed yields separated to evaluate the different risk. This framework provides estimates of the climate risk facing agricultural production in the near-term that account for the full uncertainty of climate occurrences, range of crop response, and spatial correlation in climate. In particular, the method provides robust estimates of importance of irrigation in mitigating agricultural climate risk. The results of this study can contribute to decision making about crop choice and water use in an uncertain climate.

Risk assessment of supply chain for pharmaceutical excipients with AHP-fuzzy comprehensive evaluation.

PubMed

Li, Maozhong; Du, Yunai; Wang, Qiyue; Sun, Chunmeng; Ling, Xiang; Yu, Boyang; Tu, Jiasheng; Xiong, Yerong

2016-01-01

As the essential components in formulations, pharmaceutical excipients directly affect the safety, efficacy, and stability of drugs. Recently, safety incidents of pharmaceutical excipients posing seriously threats to the patients highlight the necessity of controlling the potential risks. Hence, it is indispensable for the industry to establish an effective risk assessment system of supply chain. In this study, an AHP-fuzzy comprehensive evaluation model was developed based on the analytic hierarchy process and fuzzy mathematical theory, which quantitatively assessed the risks of supply chain. Taking polysorbate 80 as the example for model analysis, it was concluded that polysorbate 80 for injection use is a high-risk ingredient in the supply chain compared to that for oral use to achieve safety application in clinic, thus measures should be taken to control and minimize those risks.

Risk assessment of supply chain for pharmaceutical excipients with AHP-fuzzy comprehensive evaluation.

PubMed

Li, Maozhong; Du, Yunai; Wang, Qiyue; Sun, Chunmeng; Ling, Xiang; Yu, Boyang; Tu, Jiasheng; Xiong, Yerong

2016-04-01

As the essential components in formulations, pharmaceutical excipients directly affect the safety, efficacy, and stability of drugs. Recently, safety incidents of pharmaceutical excipients posing seriously threats to the patients highlight the necessity of controlling the potential risks. Hence, it is indispensable for the industry to establish an effective risk assessment system of supply chain. In this study, an AHP-fuzzy comprehensive evaluation model was developed based on the analytic hierarchy process and fuzzy mathematical theory, which quantitatively assessed the risks of supply chain. Taking polysorbate 80 as the example for model analysis, it was concluded that polysorbate 80 for injection use is a high-risk ingredient in the supply chain compared to that for oral use to achieve safety application in clinic, thus measures should be taken to control and minimize those risks.

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

76 FR 18906 - Mancozeb; Pesticide Tolerances

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-06

... from mancozeb and ETU in food as follows: i. Acute exposure. Quantitative acute dietary exposure and.... Cancer. EPA determines whether quantitative cancer exposure and risk assessments are appropriate for a... quantitative cancer risk assessment is appropriate, cancer risk may be quantified using a linear or nonlinear...

Comparison of task-based exposure metrics for an epidemiologic study of isocyanate inhalation exposures among autobody shop workers.

PubMed

Woskie, Susan R; Bello, Dhimiter; Gore, Rebecca J; Stowe, Meredith H; Eisen, Ellen A; Liu, Youcheng; Sparer, Judy A; Redlich, Carrie A; Cullen, Mark R

2008-09-01

Because many occupational epidemiologic studies use exposure surrogates rather than quantitative exposure metrics, the UMass Lowell and Yale study of autobody shop workers provided an opportunity to evaluate the relative utility of surrogates and quantitative exposure metrics in an exposure response analysis of cross-week change in respiratory function. A task-based exposure assessment was used to develop several metrics of inhalation exposure to isocyanates. The metrics included the surrogates, job title, counts of spray painting events during the day, counts of spray and bystander exposure events, and a quantitative exposure metric that incorporated exposure determinant models based on task sampling and a personal workplace protection factor for respirator use, combined with a daily task checklist. The result of the quantitative exposure algorithm was an estimate of the daily time-weighted average respirator-corrected total NCO exposure (microg/m(3)). In general, these four metrics were found to be variable in agreement using measures such as weighted kappa and Spearman correlation. A logistic model for 10% drop in FEV(1) from Monday morning to Thursday morning was used to evaluate the utility of each exposure metric. The quantitative exposure metric was the most favorable, producing the best model fit, as well as the greatest strength and magnitude of association. This finding supports the reports of others that reducing exposure misclassification can improve risk estimates that otherwise would be biased toward the null. Although detailed and quantitative exposure assessment can be more time consuming and costly, it can improve exposure-disease evaluations and is more useful for risk assessment purposes. The task-based exposure modeling method successfully produced estimates of daily time-weighted average exposures in the complex and changing autobody shop work environment. The ambient TWA exposures of all of the office workers and technicians and 57% of the painters were found to be below the current U.K. Health and Safety Executive occupational exposure limit (OEL) for total NCO of 20 microg/m(3). When respirator use was incorporated, all personal daily exposures were below the U.K. OEL.

Survival and Risk Comparison of Campylobacter jejuni on Various Processed Meat Products

PubMed Central

Hong, Soo Hyeon; Kim, Han Sol; Yoon, Ki Sun

2016-01-01

The objective of this study was to investigate survival kinetics of Campylobacter jejuni on various processed meat products (dry-cured ham, round ham with/without sodium nitrite, garlic seasoned ham with/without sodium nitrite, and sausage without sodium nitrite). Additionally, a semi-quantitative risk assessment of C. jejuni on various processed meat products was conducted using FDA-iRISK 1.0. Inoculated processed meat products with 6.0 ± 0.5 log CFU/g of C. jejuni were vacuum packed and stored at 4, 10, 17, 24, 30, and 36 °C. Survival curves were fitted to the Weibull model to obtain the delta values of C. jejuni on various processed meat products. The most rapid death of C. jejuni was observed on dry-cured ham, followed by sausage without sodium nitrite. The results of semi-quantitative risk assessment indicate that dry-cured ham represented the lowest risk among all samples. C. jejuni on processed meats presented a greater risk at 4 °C than at 10 °C. The risk of ham was greater than the risk of sausage, regardless of type. Among all samples, the highest risk of C. jejuni was observed in round ham without sodium nitrite. Overall, our data indicates that risk of processed meat products due to C. jejuni is relatively low. PMID:27294947

A new approach to hazardous materials transportation risk analysis: decision modeling to identify critical variables.

PubMed

Clark, Renee M; Besterfield-Sacre, Mary E

2009-03-01

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity, and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.

The NASA Space Radiobiology Risk Assessment Project

NASA Astrophysics Data System (ADS)

Cucinotta, Francis A.; Huff, Janice; Ponomarev, Artem; Patel, Zarana; Kim, Myung-Hee

The current first phase (2006-2011) has the three major goals of: 1) optimizing the conventional cancer risk models currently used based on the double-detriment life-table and radiation quality functions; 2) the integration of biophysical models of acute radiation syndromes; and 3) the development of new systems radiation biology models of cancer processes. The first-phase also includes continued uncertainty assessment of space radiation environmental models and transport codes, and relative biological effectiveness factors (RBE) based on flight data and NSRL results, respectively. The second phase of the (2012-2016) will: 1) develop biophysical models of central nervous system risks (CNS); 2) achieve comphrensive systems biology models of cancer processes using data from proton and heavy ion studies performed at NSRL; and 3) begin to identify computational models of biological countermeasures. Goals for the third phase (2017-2021) include: 1) the development of a systems biology model of cancer risks for operational use at NASA; 2) development of models of degenerative risks, 2) quantitative models of counter-measure impacts on cancer risks; and 3) indiviudal based risk assessments. Finally, we will support a decision point to continue NSRL research in support of NASA's exploration goals beyond 2021, and create an archival of NSRL research results for continued analysis. Details on near term goals, plans for a WEB based data resource of NSRL results, and a space radiation Wikepedia are described.

A risk hedging strategy under the nonparallel-shift yield curve

NASA Astrophysics Data System (ADS)

Gong, Pu; He, Xubiao

2005-08-01

Under the assumption of the movement of rigid, a nonparallel-shift model in the term structure of interest rates is developed by introducing Fisher & Weil duration which is a well-known concept in the area of interest risk management. This paper has studied the hedge and replication for portfolio immunization to minimize the risk exposure. Throughout the experiment of numerical simulation, the risk exposures of the portfolio under the different risk hedging strategies are quantitatively evaluated by the method of value at risk (VaR) order statistics (OS) estimation. The results show that the risk hedging strategy proposed in this paper is very effective for the interest risk management of the default-free bond.

Quantitative microbial risk assessment of microbial source tracking markers in recreational water contaminated with fresh untreated and secondary treated sewage.

PubMed

Ahmed, Warish; Hamilton, Kerry A; Lobos, Aldo; Hughes, Bridie; Staley, Christopher; Sadowsky, Michael J; Harwood, Valerie J

2018-05-14

Microbial source tracking (MST) methods have provided the means to identify sewage contamination in recreational waters, but the risk associated with elevated levels of MST targets such as sewage-associated Bacteroides HF183 and other markers is uncertain. Quantitative microbial risk assessment (QMRA) modeling allows interpretation of MST data in the context of the risk of gastrointestinal (GI) illness caused by exposure to known reference pathogens. In this study, five sewage-associated, quantitative PCR (qPCR) MST markers [Bacteroides HF183 (HF183), Methanobrevibacter smithii nifH (nifH), human adenovirus (HAdV), human polyomavirus (HPyV) and pepper mild mottle virus (PMMoV)] were evaluated to determine at what concentration these nucleic acid markers reflected a significant health risk from exposure to fresh untreated or secondary treated sewage in beach water. The QMRA models were evaluated for a target probability of illness of 36 GI illnesses/1000 swimming events (i.e., risk benchmark 0.036) for the reference pathogens norovirus (NoV) and human adenovirus 40/41 (HAdV 40/41). Sewage markers at several dilutions exceeded the risk benchmark for reference pathogens NoV and HAdV 40/41. HF183 concentrations 3.22 × 10 3 (for both NoV and HAdV 40/41) gene copies (GC)/100 mL of water contaminated with fresh untreated sewage represented risk >0.036. Similarly, HF183 concentrations 3.66 × 10 3 (for NoV and HAdV 40/41) GC/100 mL of water contaminated with secondary treated sewage represented risk >0.036. HAdV concentration as low as 4.11 × 10 1 GC/100 mL of water represented risk >0.036 when water was contaminated with secondary treated sewage. Results of this study provide a valuable context for water quality managers to evaluate human health risks associated with contamination from fresh sewage. The approach described here may also be useful in the future for evaluating health risks from contamination with aged or treated sewage or feces from other animal sources as more data are made available. Copyright © 2018 Elsevier Ltd. All rights reserved.

Katherine Young, P.E. | NREL

Science.gov Websites

) Water rights and resources engineering Database planning and development Research Interests Collection lean principles to streamline exploration and drilling and reduce error/risk Research, development and Groundwater modeling Quantitative methods in water resource engineering Water resource engineering and

Integrating Professional and Folk Models of HIV Risk: YMSM’s Perceptions of High-Risk Sex

PubMed Central

Kubicek, Katrina; Carpineto, Julie; McDavitt, Bryce; Weiss, George; Iverson, Ellen F.; Au, Chi-Wai; Kerrone, Dustin; Martinez, Miguel; Kipke, Michele D.

2009-01-01

Risks associated with HIV are well documented in research literature. While a great deal has been written about high-risk sex, little research has been conducted to examine how young men who have sex with men (YMSM) perceive and define high-risk sexual behavior. In this study, we compare the “professional’ and “folk” models of HIV-risk based on YMSM’s understanding of high-risk sex and where and how they gathered their understanding of HIV-risk behaviors. The findings reported here emerged from the quantitative and qualitative interviews from the Healthy Young Men’s Study (HYM), a longitudinal study examining risk and protective factors for substance use and sexual risk among an ethnically diverse sample of YMSM. Findings are discussed in relation to framing how service providers and others can increase YMSM’s knowledge of sexual behavior and help them build solid foundations of sexual health education to protect them from STI and HIV infection. PMID:18558819

Qualitative to quantitative: linked trajectory of method triangulation in a study on HIV/AIDS in Goa, India.

PubMed

Bailey, Ajay; Hutter, Inge

2008-10-01

With 3.1 million people estimated to be living with HIV/AIDS in India and 39.5 million people globally, the epidemic has posed academics the challenge of identifying behaviours and their underlying beliefs in the effort to reduce the risk of HIV transmission. The Health Belief Model (HBM) is frequently used to identify risk behaviours and adherence behaviour in the field of HIV/AIDS. Risk behaviour studies that apply HBM have been largely quantitative and use of qualitative methodology is rare. The marriage of qualitative and quantitative methods has never been easy. The challenge is in triangulating the methods. Method triangulation has been largely used to combine insights from the qualitative and quantitative methods but not to link both the methods. In this paper we suggest a linked trajectory of method triangulation (LTMT). The linked trajectory aims to first gather individual level information through in-depth interviews and then to present the information as vignettes in focus group discussions. We thus validate information obtained from in-depth interviews and gather emic concepts that arise from the interaction. We thus capture both the interpretation and the interaction angles of the qualitative method. Further, using the qualitative information gained, a survey is designed. In doing so, the survey questions are grounded and contextualized. We employed this linked trajectory of method triangulation in a study on the risk assessment of HIV/AIDS among migrant and mobile men. Fieldwork was carried out in Goa, India. Data come from two waves of studies, first an explorative qualitative study (2003), second a larger study (2004-2005), including in-depth interviews (25), focus group discussions (21) and a survey (n=1259). By employing the qualitative to quantitative LTMT we can not only contextualize the existing concepts of the HBM, but also validate new concepts and identify new risk groups.

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

PubMed Central

Zhang, Hongpo

2018-01-01

Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN) is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart) and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively. PMID:29854369

A quantitative risk assessment model for Vibrio parahaemolyticus in raw oysters in Sao Paulo State, Brazil.

PubMed

Sobrinho, Paulo de S Costa; Destro, Maria T; Franco, Bernadette D G M; Landgraf, Mariza

2014-06-16

A risk assessment of Vibrio parahaemolyticus associated with raw oysters produced and consumed in São Paulo State was developed. The model was built according to the United States Food and Drug Administration framework for risk assessment. The outcome of the exposure assessment estimated the prevalence and density of pathogenic V. parahaemolyticus in raw oysters from harvest to consumption. The result of the exposure step was combined with a Beta-Poisson dose-response model to estimate the probability of illness. The model predicted that the average risks per serving of raw oysters were 4.7×10(-4), 6.0×10(-4), 4.7×10(-4) and 3.1×10(-4) for spring, summer, fall and winter, respectively. Sensitivity analyses indicated that the most influential variables on the risk of illness were the total density of V. parahaemolyticus at harvest, transport temperature, relative prevalence of pathogenic strains and storage time at retail. Only storage time under refrigeration at retail showed negative correlation with the risk of illness. Copyright © 2014 Elsevier B.V. All rights reserved.

In Search of Black Swans: Identifying Students at Risk of Failing Licensing Examinations.

PubMed

Barber, Cassandra; Hammond, Robert; Gula, Lorne; Tithecott, Gary; Chahine, Saad

2018-03-01

To determine which admissions variables and curricular outcomes are predictive of being at risk of failing the Medical Council of Canada Qualifying Examination Part 1 (MCCQE1), how quickly student risk of failure can be predicted, and to what extent predictive modeling is possible and accurate in estimating future student risk. Data from five graduating cohorts (2011-2015), Schulich School of Medicine & Dentistry, Western University, were collected and analyzed using hierarchical generalized linear models (HGLMs). Area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of predictive models and determine whether they could be used to predict future risk, using the 2016 graduating cohort. Four predictive models were developed to predict student risk of failure at admissions, year 1, year 2, and pre-MCCQE1. The HGLM analyses identified gender, MCAT verbal reasoning score, two preclerkship course mean grades, and the year 4 summative objective structured clinical examination score as significant predictors of student risk. The predictive accuracy of the models varied. The pre-MCCQE1 model was the most accurate at predicting a student's risk of failing (AUC 0.66-0.93), while the admissions model was not predictive (AUC 0.25-0.47). Key variables predictive of students at risk were found. The predictive models developed suggest, while it is not possible to identify student risk at admission, we can begin to identify and monitor students within the first year. Using such models, programs may be able to identify and monitor students at risk quantitatively and develop tailored intervention strategies.

Predicting long-term performance of engineered geologic carbon dioxide storage systems to inform decisions amidst uncertainty

NASA Astrophysics Data System (ADS)

Pawar, R.

2016-12-01

Risk assessment and risk management of engineered geologic CO2 storage systems is an area of active investigation. The potential geologic CO2 storage systems currently under consideration are inherently heterogeneous and have limited to no characterization data. Effective risk management decisions to ensure safe, long-term CO2 storage requires assessing and quantifying risks while taking into account the uncertainties in a storage site's characteristics. The key decisions are typically related to definition of area of review, effective monitoring strategy and monitoring duration, potential of leakage and associated impacts, etc. A quantitative methodology for predicting a sequestration site's long-term performance is critical for making key decisions necessary for successful deployment of commercial scale geologic storage projects where projects will require quantitative assessments of potential long-term liabilities. An integrated assessment modeling (IAM) paradigm which treats a geologic CO2 storage site as a system made up of various linked subsystems can be used to predict long-term performance. The subsystems include storage reservoir, seals, potential leakage pathways (such as wellbores, natural fractures/faults) and receptors (such as shallow groundwater aquifers). CO2 movement within each of the subsystems and resulting interactions are captured through reduced order models (ROMs). The ROMs capture the complex physical/chemical interactions resulting due to CO2 movement and interactions but are computationally extremely efficient. The computational efficiency allows for performing Monte Carlo simulations necessary for quantitative probabilistic risk assessment. We have used the IAM to predict long-term performance of geologic CO2 sequestration systems and to answer questions related to probability of leakage of CO2 through wellbores, impact of CO2/brine leakage into shallow aquifer, etc. Answers to such questions are critical in making key risk management decisions. A systematic uncertainty quantification approach can been used to understand how uncertain parameters associated with different subsystems (e.g., reservoir permeability, wellbore cement permeability, wellbore density, etc.) impact the overall site performance predictions.

Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance.

PubMed

Sammut, Eva C; Villa, Adriana D M; Di Giovine, Gabriella; Dancy, Luke; Bosio, Filippo; Gibbs, Thomas; Jeyabraba, Swarna; Schwenke, Susanne; Williams, Steven E; Marber, Michael; Alfakih, Khaled; Ismail, Tevfik F; Razavi, Reza; Chiribiri, Amedeo

2018-05-01

This study sought to evaluate the prognostic usefulness of visual and quantitative perfusion cardiac magnetic resonance (CMR) ischemic burden in an unselected group of patients and to assess the validity of consensus-based ischemic burden thresholds extrapolated from nuclear studies. There are limited data on the prognostic value of assessing myocardial ischemic burden by CMR, and there are none using quantitative perfusion analysis. Patients with suspected coronary artery disease referred for adenosine-stress perfusion CMR were included (n = 395; 70% male; age 58 ± 13 years). The primary endpoint was a composite of cardiovascular death, nonfatal myocardial infarction, aborted sudden death, and revascularization after 90 days. Perfusion scans were assessed visually and with quantitative analysis. Cross-validated Cox regression analysis and net reclassification improvement were used to assess the incremental prognostic value of visual or quantitative perfusion analysis over a baseline clinical model, initially as continuous covariates, then using accepted thresholds of ≥2 segments or ≥10% myocardium. After a median 460 days (interquartile range: 190 to 869 days) follow-up, 52 patients reached the primary endpoint. At 2 years, the addition of ischemic burden was found to increase prognostic value over a baseline model of age, sex, and late gadolinium enhancement (baseline model area under the curve [AUC]: 0.75; visual AUC: 0.84; quantitative AUC: 0.85). Dichotomized quantitative ischemic burden performed better than visual assessment (net reclassification improvement 0.043 vs. 0.003 against baseline model). This study was the first to address the prognostic benefit of quantitative analysis of perfusion CMR and to support the use of consensus-based ischemic burden thresholds by perfusion CMR for prognostic evaluation of patients with suspected coronary artery disease. Quantitative analysis provided incremental prognostic value to visual assessment and established risk factors, potentially representing an important step forward in the translation of quantitative CMR perfusion analysis to the clinical setting. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Towards a Quantitative Framework for Evaluating Vulnerability of Drinking Water Wells to Contamination from Unconventional Oil & Gas Development

NASA Astrophysics Data System (ADS)

Soriano, M., Jr.; Deziel, N. C.; Saiers, J. E.

2017-12-01

The rapid expansion of unconventional oil and gas (UO&G) production, made possible by advances in hydraulic fracturing (fracking), has triggered concerns over risks this extraction poses to water resources and public health. Concerns are particularly acute within communities that host UO&G development and rely heavily on shallow aquifers as sources of drinking water. This research aims to develop a quantitative framework to evaluate the vulnerability of drinking water wells to contamination from UO&G activities. The concept of well vulnerability is explored through application of backwards travel time probability modeling to estimate the likelihood that capture zones of drinking water wells circumscribe source locations of UO&G contamination. Sources of UO&G contamination considered in this analysis include gas well pads and documented sites of UO&G wastewater and chemical spills. The modeling approach is illustrated for a portion of Susquehanna County, Pennsylvania, where more than one thousand shale gas wells have been completed since 2005. Data from a network of eight multi-level groundwater monitoring wells installed in the study site in 2015 are used to evaluate the model. The well vulnerability concept is proposed as a physically based quantitative tool for policy-makers dealing with the management of contamination risks of drinking water wells. In particular, the model can be used to identify adequate setback distances of UO&G activities from drinking water wells and other critical receptors.

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

PubMed Central

Rieger, TR; Musante, CJ

2016-01-01

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed “virtual patients.” In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations. PMID:27069777

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

Application of quantitative microbial risk assessments for estimation of risk management metrics: Clostridium perfringens in ready-to-eat and partially cooked meat and poultry products as an example.

PubMed

Crouch, Edmund A; Labarre, David; Golden, Neal J; Kause, Janell R; Dearfield, Kerry L

2009-10-01

The U.S. Department of Agriculture, Food Safety and Inspection Service is exploring quantitative risk assessment methodologies to incorporate the use of the Codex Alimentarius' newly adopted risk management metrics (e.g., food safety objectives and performance objectives). It is suggested that use of these metrics would more closely tie the results of quantitative microbial risk assessments (QMRAs) to public health outcomes. By estimating the food safety objective (the maximum frequency and/or concentration of a hazard in a food at the time of consumption) and the performance objective (the maximum frequency and/or concentration of a hazard in a food at a specified step in the food chain before the time of consumption), risk managers will have a better understanding of the appropriate level of protection (ALOP) from microbial hazards for public health protection. We here demonstrate a general methodology that allows identification of an ALOP and evaluation of corresponding metrics at appropriate points in the food chain. It requires a two-dimensional probabilistic risk assessment, the example used being the Monte Carlo QMRA for Clostridium perfringens in ready-to eat and partially cooked meat and poultry products, with minor modifications to evaluate and abstract required measures. For demonstration purposes, the QMRA model was applied specifically to hot dogs produced and consumed in the United States. Evaluation of the cumulative uncertainty distribution for illness rate allows a specification of an ALOP that, with defined confidence, corresponds to current industry practices.

Risk Assessment and Integration Team (RAIT) Portfolio Risk Analysis Strategy

NASA Technical Reports Server (NTRS)

Edwards, Michelle

2010-01-01

Impact at management level: Qualitative assessment of risk criticality in conjunction with risk consequence, likelihood, and severity enable development of an "investment policy" towards managing a portfolio of risks. Impact at research level: Quantitative risk assessments enable researchers to develop risk mitigation strategies with meaningful risk reduction results. Quantitative assessment approach provides useful risk mitigation information.

The SAM framework: modeling the effects of management factors on human behavior in risk analysis.

PubMed

Murphy, D M; Paté-Cornell, M E

1996-08-01

Complex engineered systems, such as nuclear reactors and chemical plants, have the potential for catastrophic failure with disastrous consequences. In recent years, human and management factors have been recognized as frequent root causes of major failures in such systems. However, classical probabilistic risk analysis (PRA) techniques do not account for the underlying causes of these errors because they focus on the physical system and do not explicitly address the link between components' performance and organizational factors. This paper describes a general approach for addressing the human and management causes of system failure, called the SAM (System-Action-Management) framework. Beginning with a quantitative risk model of the physical system, SAM expands the scope of analysis to incorporate first the decisions and actions of individuals that affect the physical system. SAM then links management factors (incentives, training, policies and procedures, selection criteria, etc.) to those decisions and actions. The focus of this paper is on four quantitative models of action that describe this last relationship. These models address the formation of intentions for action and their execution as a function of the organizational environment. Intention formation is described by three alternative models: a rational model, a bounded rationality model, and a rule-based model. The execution of intentions is then modeled separately. These four models are designed to assess the probabilities of individual actions from the perspective of management, thus reflecting the uncertainties inherent to human behavior. The SAM framework is illustrated for a hypothetical case of hazardous materials transportation. This framework can be used as a tool to increase the safety and reliability of complex technical systems by modifying the organization, rather than, or in addition to, re-designing the physical system.

Quantitative risk assessment of CO2 transport by pipelines--a review of uncertainties and their impacts.

PubMed

Koornneef, Joris; Spruijt, Mark; Molag, Menso; Ramírez, Andrea; Turkenburg, Wim; Faaij, André

2010-05-15

A systematic assessment, based on an extensive literature review, of the impact of gaps and uncertainties on the results of quantitative risk assessments (QRAs) for CO(2) pipelines is presented. Sources of uncertainties that have been assessed are: failure rates, pipeline pressure, temperature, section length, diameter, orifice size, type and direction of release, meteorological conditions, jet diameter, vapour mass fraction in the release and the dose-effect relationship for CO(2). A sensitivity analysis with these parameters is performed using release, dispersion and impact models. The results show that the knowledge gaps and uncertainties have a large effect on the accuracy of the assessed risks of CO(2) pipelines. In this study it is found that the individual risk contour can vary between 0 and 204 m from the pipeline depending on assumptions made. In existing studies this range is found to be between <1m and 7.2 km. Mitigating the relevant risks is part of current practice, making them controllable. It is concluded that QRA for CO(2) pipelines can be improved by validation of release and dispersion models for high-pressure CO(2) releases, definition and adoption of a universal dose-effect relationship and development of a good practice guide for QRAs for CO(2) pipelines. Copyright (c) 2009 Elsevier B.V. All rights reserved.

Using metal-ligand binding characteristics to predict metal toxicity: quantitative ion character-activity relationships (QICARs).

PubMed Central

Newman, M C; McCloskey, J T; Tatara, C P

1998-01-01

Ecological risk assessment can be enhanced with predictive models for metal toxicity. Modelings of published data were done under the simplifying assumption that intermetal trends in toxicity reflect relative metal-ligand complex stabilities. This idea has been invoked successfully since 1904 but has yet to be applied widely in quantitative ecotoxicology. Intermetal trends in toxicity were successfully modeled with ion characteristics reflecting metal binding to ligands for a wide range of effects. Most models were useful for predictive purposes based on an F-ratio criterion and cross-validation, but anomalous predictions did occur if speciation was ignored. In general, models for metals with the same valence (i.e., divalent metals) were better than those combining mono-, di-, and trivalent metals. The softness parameter (sigma p) and the absolute value of the log of the first hydrolysis constant ([symbol: see text] log KOH [symbol: see text]) were especially useful in model construction. Also, delta E0 contributed substantially to several of the two-variable models. In contrast, quantitative attempts to predict metal interactions in binary mixtures based on metal-ligand complex stabilities were not successful. PMID:9860900

Risk appreciation for living kidney donors: another new subspecialty?

PubMed

Steiner, Robert W

2004-05-01

Quantitative estimates of the risk of end stage renal disease (ESRD) for living donors would seem essential to defensible donor selection practices, as the 'safe/unsafe' model for donor selection is not viable. All kidney donors take risk, and four fundamental, qualitative criteria should instead be used to decide when donor rejection is justified. These criteria are lack of donor education about transplantation, donor irrationality, lack of free and voluntary donation, and/or that donor acceptance would unavoidably threaten the public trust or the integrity of the center's selection procedures. Such a data-based selection policy, with explicit documentation of unbiased and comprehensive donor education, will help neutralize the center's self interest in a more defensible way than by rejecting 'complicated' kidney donors out of hand, and in a more practical way than by the creation of center-independent donor counselors or waiting for donor registries to come to fruition. Living kidney donors with isolated medical abnormalities comprise a sizable subset of at risk donors for whom center acceptance practices vary markedly. This population provides a paradigm opportunity for quantitative risk estimation and counseling.

Characterizing the risk of infection from Mycobacterium tuberculosis in commercial passenger aircraft using quantitative microbial risk assessment.

PubMed

Jones, Rachael M; Masago, Yoshifumi; Bartrand, Timothy; Haas, Charles N; Nicas, Mark; Rose, Joan B

2009-03-01

Quantitative microbial risk assessment was used to predict the likelihood and spatial organization of Mycobacterium tuberculosis (Mtb) transmission in a commercial aircraft. Passenger exposure was predicted via a multizone Markov model in four scenarios: seated or moving infectious passengers and with or without filtration of recirculated cabin air. The traditional exponential (k = 1) and a new exponential (k = 0.0218) dose-response function were used to compute infection risk. Emission variability was included by Monte Carlo simulation. Infection risks were higher nearer and aft of the source; steady state airborne concentration levels were not attained. Expected incidence was low to moderate, with the central 95% ranging from 10(-6) to 10(-1) per 169 passengers in the four scenarios. Emission rates used were low compared to measurements from active TB patients in wards, thus a "superspreader" emitting 44 quanta/h could produce 6.2 cases or more under these scenarios. Use of respiratory protection by the infectious source and/or susceptible passengers reduced infection incidence up to one order of magnitude.

Including operational data in QMRA model: development and impact of model inputs.

PubMed

Jaidi, Kenza; Barbeau, Benoit; Carrière, Annie; Desjardins, Raymond; Prévost, Michèle

2009-03-01

A Monte Carlo model, based on the Quantitative Microbial Risk Analysis approach (QMRA), has been developed to assess the relative risks of infection associated with the presence of Cryptosporidium and Giardia in drinking water. The impact of various approaches for modelling the initial parameters of the model on the final risk assessments is evaluated. The Monte Carlo simulations that we performed showed that the occurrence of parasites in raw water was best described by a mixed distribution: log-Normal for concentrations > detection limit (DL), and a uniform distribution for concentrations < DL. The selection of process performance distributions for modelling the performance of treatment (filtration and ozonation) influences the estimated risks significantly. The mean annual risks for conventional treatment are: 1.97E-03 (removal credit adjusted by log parasite = log spores), 1.58E-05 (log parasite = 1.7 x log spores) or 9.33E-03 (regulatory credits based on the turbidity measurement in filtered water). Using full scale validated SCADA data, the simplified calculation of CT performed at the plant was shown to largely underestimate the risk relative to a more detailed CT calculation, which takes into consideration the downtime and system failure events identified at the plant (1.46E-03 vs. 3.93E-02 for the mean risk).

Considerations in deriving quantitative cancer criteria for inorganic arsenic exposure via inhalation.

PubMed

Lewis, Ari S; Beyer, Leslie A; Zu, Ke

2015-01-01

The inhalation unit risk (IUR) that currently exists in the United States Environmental Protection Agency's (US EPA's) Integrated Risk Information System was developed in 1984 based on studies examining the relationship between respiratory cancer and arsenic exposure in copper smelters from two US locations: the copper smelter in Anaconda, Montana, and the American Smelting And Refining COmpany (ASARCO) smelter in Tacoma, Washington. Since US EPA last conducted its assessment, additional data have become available from epidemiology and mechanistic studies. In addition, the California Air Resources Board, Texas Commission of Environmental Quality, and Dutch Expert Committee on Occupational Safety have all conducted new risk assessments. All three analyses, which calculated IURs based on respiratory/lung cancer mortality, generated IURs that are lower (i.e., less restrictive) than the current US EPA value of 4.3×10(-3) (μg/m(3))(-1). The IURs developed by these agencies, which vary more than 20-fold, are based on somewhat different studies and use different methodologies to address uncertainties in the underlying datasets. Despite these differences, all were developed based on a cumulative exposure metric assuming a low-dose linear dose-response relationship. In this paper, we contrast and compare the analyses conducted by these agencies and critically evaluate strengths and limitations inherent in the data and methodologies used to develop quantitative risk estimates. In addition, we consider how these data could be best used to assess risk at much lower levels of arsenic in air, such as those experienced by the general public. Given that the mode of action for arsenic supports a threshold effect, and epidemiological evidence suggests that the arsenic concentration in air is a reliable predictor of lung/respiratory cancer risk, we developed a quantitative cancer risk analysis using a nonlinear threshold model. Applying a nonlinear model to occupational data, we established points of departure based on both cumulative exposure (μg/m(3)-years) to arsenic and arsenic concentration (μg/m(3)) via inhalation. Using these values, one can assess the lifetime risk of respiratory cancer mortality associated with ambient air concentrations of arsenic for the general US population. Copyright © 2014 Elsevier Ltd. All rights reserved.

Arenal-type pyroclastic flows: A probabilistic event tree risk analysis

NASA Astrophysics Data System (ADS)

Meloy, Anthony F.

2006-09-01

A quantitative hazard-specific scenario-modelling risk analysis is performed at Arenal volcano, Costa Rica for the newly recognised Arenal-type pyroclastic flow (ATPF) phenomenon using an event tree framework. These flows are generated by the sudden depressurisation and fragmentation of an active basaltic andesite lava pool as a result of a partial collapse of the crater wall. The deposits of this type of flow include angular blocks and juvenile clasts, which are rarely found in other types of pyroclastic flow. An event tree analysis (ETA) is a useful tool and framework in which to analyse and graphically present the probabilities of the occurrence of many possible events in a complex system. Four event trees are created in the analysis, three of which are extended to investigate the varying individual risk faced by three generic representatives of the surrounding community: a resident, a worker, and a tourist. The raw numerical risk estimates determined by the ETA are converted into a set of linguistic expressions (i.e. VERY HIGH, HIGH, MODERATE etc.) using an established risk classification scale. Three individually tailored semi-quantitative risk maps are then created from a set of risk conversion tables to show how the risk varies for each individual in different areas around the volcano. In some cases, by relocating from the north to the south, the level of risk can be reduced by up to three classes. While the individual risk maps may be broadly applicable, and therefore of interest to the general community, the risk maps and associated probability values generated in the ETA are intended to be used by trained professionals and government agencies to evaluate the risk and effectively manage the long-term development of infrastructure and habitation. With the addition of fresh monitoring data, the combination of both long- and short-term event trees would provide a comprehensive and consistent method of risk analysis (both during and pre-crisis), and as such, an ETA is considered to be a valuable quantitative decision support tool.

Leisure-time physical activity and incident metabolic syndrome: a systematic review and dose-response meta-analysis of cohort studies.

PubMed

Zhang, Dongdong; Liu, Xuejiao; Liu, Yu; Sun, Xizhuo; Wang, Bingyuan; Ren, Yongcheng; Zhao, Yang; Zhou, Junmei; Han, Chengyi; Yin, Lei; Zhao, Jingzhi; Shi, Yuanyuan; Zhang, Ming; Hu, Dongsheng

2017-10-01

Leisure-time physical activity (LTPA) has been suggested to reduce risk of metabolic syndrome (MetS). However, a quantitative comprehensive assessment of the dose-response association between LTPA and incident MetS has not been reported. We performed a meta-analysis of studies assessing the risk of MetS with LTPA. MEDLINE via PubMed and EMBase databases were searched for relevant articles published up to March 13, 2017. Random-effects models were used to estimate the summary relative risk (RR) of MetS with LTPA. Restricted cubic splines were used to model the dose-response association. We identified 16 articles (18 studies including 76,699 participants and 13,871 cases of MetS). We found a negative linear association between LTPA and incident MetS, with a reduction of 8% in MetS risk per 10 metabolic equivalent of task (MET) h/week increment. According to the restricted cubic splines model, risk of MetS was reduced 10% with LTPA performed according to the basic guideline-recommended level of 150min of moderate PA (MPA) per week (10METh/week) versus inactivity (RR=0.90, 95% CI 0.86-0.94). It was reduced 20% and 53% with LTPA at twice (20METh/week) and seven times (70METh/week) the basic recommended level (RR=0.80, 95% CI 0.74-0.88 and 0.47, 95% CI 0.34-0.64, respectively). Our findings provide quantitative data suggesting that any amount of LTPA is better than none and that LTPA substantially exceeding the current LTPA guidelines is associated with an additional reduction in MetS risk. Copyright © 2017. Published by Elsevier Inc.

Overcoming confirmation bias in causal attribution: a case study of antibiotic resistance risks.

PubMed

Cox, Louis Anthony Tony; Popken, Douglas A

2008-10-01

When they do not use formal quantitative risk assessment methods, many scientists (like other people) make mistakes and exhibit biases in reasoning about causation, if-then relations, and evidence. Decision-related conclusions or causal explanations are reached prematurely based on narrative plausibility rather than adequate factual evidence. Then, confirming evidence is sought and emphasized, but disconfirming evidence is ignored or discounted. This tendency has serious implications for health-related public policy discussions and decisions. We provide examples occurring in antimicrobial health risk assessments, including a case study of a recently reported positive relation between virginiamycin (VM) use in poultry and risk of resistance to VM-like (streptogramin) antibiotics in humans. This finding has been used to argue that poultry consumption causes increased resistance risks, that serious health impacts may result, and therefore use of VM in poultry should be restricted. However, the original study compared healthy vegetarians to hospitalized poultry consumers. Our examination of the same data using conditional independence tests for potential causality reveals that poultry consumption acted as a surrogate for hospitalization in this study. After accounting for current hospitalization status, no evidence remains supporting a causal relationship between poultry consumption and increased streptogramin resistance. This example emphasizes both the importance and the practical possibility of analyzing and presenting quantitative risk information using data analysis techniques (such as Bayesian model averaging (BMA) and conditional independence tests) that are as free as possible from potential selection, confirmation, and modeling biases.

How to model a negligible probability under the WTO sanitary and phytosanitary agreement?

PubMed

Powell, Mark R

2013-06-01

Since the 1997 EC--Hormones decision, World Trade Organization (WTO) Dispute Settlement Panels have wrestled with the question of what constitutes a negligible risk under the Sanitary and Phytosanitary Agreement. More recently, the 2010 WTO Australia--Apples Panel focused considerable attention on the appropriate quantitative model for a negligible probability in a risk assessment. The 2006 Australian Import Risk Analysis for Apples from New Zealand translated narrative probability statements into quantitative ranges. The uncertainty about a "negligible" probability was characterized as a uniform distribution with a minimum value of zero and a maximum value of 10(-6) . The Australia - Apples Panel found that the use of this distribution would tend to overestimate the likelihood of "negligible" events and indicated that a triangular distribution with a most probable value of zero and a maximum value of 10⁻⁶ would correct the bias. The Panel observed that the midpoint of the uniform distribution is 5 × 10⁻⁷ but did not consider that the triangular distribution has an expected value of 3.3 × 10⁻⁷. Therefore, if this triangular distribution is the appropriate correction, the magnitude of the bias found by the Panel appears modest. The Panel's detailed critique of the Australian risk assessment, and the conclusions of the WTO Appellate Body about the materiality of flaws found by the Panel, may have important implications for the standard of review for risk assessments under the WTO SPS Agreement. © 2012 Society for Risk Analysis.

[Risk, uncertainty and ignorance in medicine].

PubMed

Rørtveit, G; Strand, R

2001-04-30

Exploration of healthy patients' risk factors for disease has become a major medical activity. The rationale behind primary prevention through exploration and therapeutic risk reduction is not separated from the theoretical assumption that every form of uncertainty can be expressed as risk. Distinguishing "risk" (as quantitative probabilities in a known sample space), "strict uncertainty" (when the sample space is known, but probabilities of events cannot be quantified) and "ignorance" (when the sample space is not fully known), a typical clinical situation (primary risk of coronary disease) is analysed. It is shown how strict uncertainty and sometimes ignorance can be present, in which case the orthodox decision theoretical rationale for treatment breaks down. For use in such cases, a different ideal model of rationality is proposed, focusing on the patient's considered reasons. This model has profound implications for the current understanding of medical professionalism as well as for the design of clinical guidelines.

ADVANCED COMPUTATIONAL METHODS IN DOSE MODELING

EPA Science Inventory

The overall goal of the EPA-ORD NERL research program on Computational Toxicology (CompTox) is to provide the Agency with the tools of modern chemistry, biology, and computing to improve quantitative risk assessments and reduce uncertainties in the source-to-adverse outcome conti...

Physiologically Based Pharmacokinetic (PBPK) Modeling of Interstrain Variability in Trichloroethylene Metabolism in the Mouse

EPA Science Inventory

Background: Quantitative estimation of toxicokinetic variability in the human population is a persistent challenge in risk assessment of environmental chemicals. Traditionally, inter-individual differences in the population are accounted for by default assumptions or, in rare cas...

A Quantitative Risk Analysis of Deficient Contractor Business System

DTIC Science & Technology

2012-04-30

Mathematically , Jorion’s concept of VaR looks like this: ( > ) ≤ 1 − (2) where, = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=éêçÖê~ãW= `êÉ~íáåÖ=póåÉêÖó=Ñçê=fåÑçêãÉÇ=ÅÜ...presents three models for calculating VaR. The local-valuation method determines the value of a portfolio once and uses mathematical derivatives...management. In the insurance industry, actuarial data is applied to model risk and risk capital reserves are “held” to cover the expected values for

Quantitative risk assessment using empirical vulnerability functions from debris flow event reconstruction

NASA Astrophysics Data System (ADS)

Luna, Byron Quan; Blahut, Jan; Camera, Corrado; van Westen, Cees; Sterlacchini, Simone; Apuani, Tiziana; Akbas, Sami

2010-05-01

For a quantitative risk assessment framework it is essential to assess not only the hazardous process itself but to perform an analysis of their consequences. This quantitative assessment should include the expected monetary losses as the product of the probability of occurrence of a hazard with a given magnitude and its vulnerability. A quantifiable integrated approach of both hazard and risk is becoming a required practice in risk reduction management. Dynamic run-out models for debris flows are able to calculate physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk could suffer an impact. These results are then applied for vulnerability and risk calculations. The risk assessment has been conducted in the Valtellina Valley, a typical Italian alpine valley lying in northern Italy (Lombardy Region). On 13th July 2008, after more than two days of intense rainfall, several debris and mud flows were released in the central part of valley between Morbegno and Berbenno. One of the largest debris flows occurred in Selvetta. The debris flow event was reconstructed after extensive field work and interviews with local inhabitants and civil protection teams. Also inside the Valtellina valley, between the 22nd and the 23rd of May 1983, two debris flows happened in Tresenda (Teglio municipality), causing casualties and considerable economic damages. On the same location, during the 26th of November 2002, another debris flow occurred that caused significant damage. For the quantification of a new scenario, the outcome results obtained from the event of Selvetta were applied in Tresenda. The Selvetta and Tresenda event were modelled with the FLO2D program. FLO2D is an Eulerian formulation with a finite differences numerical scheme that requires the specification of an input hydrograph. The internal stresses are isotropic and the basal shear stresses are calculated using a quadratic model. The significance of calculated values of pressure and velocity were investigated in terms of the resulting damage to the affected buildings. The physical damage was quantified for each affected structure within the context of physical vulnerability, which is defined as the ratio between the monetary loss and the reconstruction value. Two different empirical vulnerability curves were obtained, which are functions of debris flow velocity and pressure, respectively. Prospective economic direct losses were estimated.

Critical asset and portfolio risk analysis: an all-hazards framework.

PubMed

Ayyub, Bilal M; McGill, William L; Kaminskiy, Mark

2007-08-01

This article develops a quantitative all-hazards framework for critical asset and portfolio risk analysis (CAPRA) that considers both natural and human-caused hazards. Following a discussion on the nature of security threats, the need for actionable risk assessments, and the distinction between asset and portfolio-level analysis, a general formula for all-hazards risk analysis is obtained that resembles the traditional model based on the notional product of consequence, vulnerability, and threat, though with clear meanings assigned to each parameter. Furthermore, a simple portfolio consequence model is presented that yields first-order estimates of interdependency effects following a successful attack on an asset. Moreover, depending on the needs of the decisions being made and available analytical resources, values for the parameters in this model can be obtained at a high level or through detailed systems analysis. Several illustrative examples of the CAPRA methodology are provided.

Rock Slide Risk Assessment: A Semi-Quantitative Approach

NASA Astrophysics Data System (ADS)

Duzgun, H. S. B.

2009-04-01

Rock slides can be better managed by systematic risk assessments. Any risk assessment methodology for rock slides involves identification of rock slide risk components, which are hazard, elements at risk and vulnerability. For a quantitative/semi-quantitative risk assessment for rock slides, a mathematical value the risk has to be computed and evaluated. The quantitative evaluation of risk for rock slides enables comparison of the computed risk with the risk of other natural and/or human-made hazards and providing better decision support and easier communication for the decision makers. A quantitative/semi-quantitative risk assessment procedure involves: Danger Identification, Hazard Assessment, Elements at Risk Identification, Vulnerability Assessment, Risk computation, Risk Evaluation. On the other hand, the steps of this procedure require adaptation of existing or development of new implementation methods depending on the type of landslide, data availability, investigation scale and nature of consequences. In study, a generic semi-quantitative risk assessment (SQRA) procedure for rock slides is proposed. The procedure has five consecutive stages: Data collection and analyses, hazard assessment, analyses of elements at risk and vulnerability and risk assessment. The implementation of the procedure for a single rock slide case is illustrated for a rock slope in Norway. Rock slides from mountain Ramnefjell to lake Loen are considered to be one of the major geohazards in Norway. Lake Loen is located in the inner part of Nordfjord in Western Norway. Ramnefjell Mountain is heavily jointed leading to formation of vertical rock slices with height between 400-450 m and width between 7-10 m. These slices threaten the settlements around Loen Valley and tourists visiting the fjord during summer season, as the released slides have potential of creating tsunami. In the past, several rock slides had been recorded from the Mountain Ramnefjell between 1905 and 1950. Among them, four of the slides caused formation of tsunami waves which washed up to 74 m above the lake level. Two of the slides resulted in many fatalities in the inner part of the Loen Valley as well as great damages. There are three predominant joint structures in Ramnefjell Mountain, which controls failure and the geometry of the slides. The first joint set is a foliation plane striking northeast-southwest and dipping 35˚ -40˚ to the east-southeast. The second and the third joint sets are almost perpendicular and parallel to the mountain side and scarp, respectively. These three joint sets form slices of rock columns with width ranging between 7-10 m and height of 400-450 m. It is stated that the joints in set II are opened between 1-2 m, which may bring about collection of water during heavy rainfall or snow melt causing the slices to be pressed out. It is estimated that water in the vertical joints both reduces the shear strength of sliding plane and causes reduction of normal stress on the sliding plane due to formation of uplift force. Hence rock slides in Ramnefjell mountain occur in plane failure mode. The quantitative evaluation of rock slide risk requires probabilistic analysis of rock slope stability and identification of consequences if the rock slide occurs. In this study failure probability of a rock slice is evaluated by first-order reliability method (FORM). Then in order to use the calculated probability of failure value (Pf) in risk analyses, it is required to associate this Pf with frequency based probabilities (i.ePf / year) since the computed failure probabilities is a measure of hazard and not a measure of risk unless they are associated with the consequences of the failure. This can be done by either considering the time dependent behavior of the basic variables in the probabilistic models or associating the computed Pf with frequency of the failures in the region. In this study, the frequency of previous rock slides in the previous century in Remnefjell is used for evaluation of frequency based probability to be used in risk assessment. The major consequence of a rock slide is generation of a tsunami in the lake Loen, causing inundation of residential areas around the lake. Risk is assessed by adapting damage probability matrix approach, which is originally developed for risk assessment for buildings in case of earthquake.

Operational models of infrastructure resilience.

PubMed

Alderson, David L; Brown, Gerald G; Carlyle, W Matthew

2015-04-01

We propose a definition of infrastructure resilience that is tied to the operation (or function) of an infrastructure as a system of interacting components and that can be objectively evaluated using quantitative models. Specifically, for any particular system, we use quantitative models of system operation to represent the decisions of an infrastructure operator who guides the behavior of the system as a whole, even in the presence of disruptions. Modeling infrastructure operation in this way makes it possible to systematically evaluate the consequences associated with the loss of infrastructure components, and leads to a precise notion of "operational resilience" that facilitates model verification, validation, and reproducible results. Using a simple example of a notional infrastructure, we demonstrate how to use these models for (1) assessing the operational resilience of an infrastructure system, (2) identifying critical vulnerabilities that threaten its continued function, and (3) advising policymakers on investments to improve resilience. © 2014 Society for Risk Analysis.

Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990-2030)

Treesearch

R.K. Meentemeyer; N.J. Cunniffe; A.R. Cook; J.A.N. Filipe; R.D. Hunter; D.M. Rizzo; C.A. Gilligan

2011-01-01

The spread of emerging infectious diseases (EIDs) in natural environments poses substantial risks to biodiversity and ecosystem function. As EIDs and their impacts grow, landscape- to regional-scale models of disease dynamics are increasingly needed for quantitative prediction of epidemic outcomes and design of practicable strategies for control. Here we use spatio-...

Mathematical Modeling in Support of Military Operational Medicine

DTIC Science & Technology

2006-07-01

of PBPK models in toxicol- ogy research and chemical risk assessment today is pri- marily related to their ability to make more quantitative ...derived earlier. The biomechanical basis of SFC is established by its correlation with strain. (a) Pretest Scan (b) Posttest Fracture...Three-Dimensional Reconstruction of Pretest and Posttest CT Scans Cited References Vander Vorst, M., Stuhmiller, J., et al., (2003). Biomechanically

Bayesian data assimilation provides rapid decision support for vector-borne diseases

PubMed Central

Jewell, Chris P.; Brown, Richard G.

2015-01-01

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. PMID:26136225

[Hamburger consumption patterns and exposure assessment for verocytotoxigenic Escherichia coli (VTEC): simulation model].

PubMed

Signorini, M L; Marín, V; Quinteros, C; Tarabla, H

2009-01-01

A quantitative risk assessment was developed for verocytotoxigenic Escherichia coli (VTEC) associated with hamburger consumption. The assessment (simulation model) considers the distribution, storage and consumption patterns of hamburgers. The prevalence and concentration of VTEC were modelled at various stages along the agri-food beef production system using input derived from Argentinean data, whenever possible. The model predicted an infection risk of 4.45 x 10(-4) per meal for adults. The risk values obtained for children were 2.6 x 10(-4), 1.38 x 10(-5) and 4.54 x 10(-7) for infection, Hemolytic Uremic Syndrome (HUS) and mortality, respectively. The risk of infection and HUS was positively correlated with bacterial concentration in meat (r = 0.664). There was a negative association between homemade hamburgers (r = -0.116) and the risk of illness; however this association has been considered due to differences between retail and domiciliary storage systems (r = -0.567) and not because of the intrinsic characteristics of the product. The most sensitive points of the production system were identified through the risk assessment, therefore, these can be utilized as a basis to apply different risk management policies in public health.

Online Information Sharing About Risks: The Case of Organic Food.

PubMed

Hilverda, Femke; Kuttschreuter, Margôt

2018-03-23

Individuals have to make sense of an abundance of information to decide whether or not to purchase certain food products. One of the means to sense-making is information sharing. This article reports on a quantitative study examining online information sharing behavior regarding the risks of organic food products. An online survey among 535 respondents was conducted in the Netherlands to examine the determinants of information sharing behavior, and their relationships. Structural equation modeling was applied to test both the measurement model and the structural model. Results showed that the intention to share information online about the risks of organic food was low. Conversations and email were the preferred channels to share information; of the social media Facebook stood out. The developed model was found to provide an adequate description of the data. It explained 41% of the variance in information sharing. Injunctive norms and outcome expectancies were most important in predicting online information sharing, followed by information-related determinants. Risk-perception-related determinants showed a significant, but weak, positive relationship with online information sharing. Implications for authorities communicating on risks associated with food are addressed. © 2018 The Authors Risk Analysis published by Wiley Periodicals, Inc. on behalf of Society for Risk Analysis.

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.

Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India.

PubMed

Das, Iswar; Kumar, Gaurav; Stein, Alfred; Bagchi, Arunabha; Dadhwal, Vinay K

2011-07-01

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment.

Adolescent Gender-Related Abuse, Androphilia, and HIV Risk Among Transfeminine People of Color in New York City

PubMed Central

Hwahng, Sel J.; Nuttbrock, Larry

2014-01-01

Public health research has indicated extremely high HIV seroprevalence (13–63%) among low-income transfeminine people of color of African, Latina, and Asian descent living in the U.S. This paper combines two data sets. One set is based on an ethnographic study (N=50, 120 hours of participant observation). The other set longitudinal quantitative study (baseline N=600, N=275 followed for 3 years). Transfeminine people of color are much more likely to be androphilic and at high HIV risk. A greater understanding of adolescent gender-related abuse and trauma-impacted androphilia contributes towards a holistic conceptual model of HIV risk. A theoretical model is proposed that incorporates findings from both studies and integrates sociostructural, interpersonal, and intrapsychic levels of HIV risk. PMID:24294927

Risk management modeling and its application in maritime safety

NASA Astrophysics Data System (ADS)

Qin, Ting-Rong; Chen, Wei-Jiong; Zeng, Xiang-Kun

2008-12-01

Quantified risk assessment (QRA) needs mathematicization of risk theory. However, attention has been paid almost exclusively to applications of assessment methods, which has led to neglect of research into fundamental theories, such as the relationships among risk, safety, danger, and so on. In order to solve this problem, as a first step, fundamental theoretical relationships about risk and risk management were analyzed for this paper in the light of mathematics, and then illustrated with some charts. Second, man-machine-environment-management (MMEM) theory was introduced into risk theory to analyze some properties of risk. On the basis of this, a three-dimensional model of risk management was established that includes: a goal dimension; a management dimension; an operation dimension. This goal management operation (GMO) model was explained and then emphasis was laid on the discussion of the risk flowchart (operation dimension), which lays the groundwork for further study of risk management and qualitative and quantitative assessment. Next, the relationship between Formal Safety Assessment (FSA) and Risk Management was researched. This revealed that the FSA method, which the international maritime organization (IMO) is actively spreading, comes from Risk Management theory. Finally, conclusion were made about how to apply this risk management method to concrete fields efficiently and conveniently, as well as areas where further research is required.

A simulation model of IT risk on program trading

NASA Astrophysics Data System (ADS)

Xia, Bingying; Jiang, Wenbao; Luo, Guangxuan

2015-12-01

The biggest difficulty for Program trading IT risk measures lies in the loss of data, in view of this situation, the current scholars approach is collecting court, network and other public media such as all kinds of accident of IT both at home and abroad for data collection, and the loss of IT risk quantitative analysis based on this database. However, the IT risk loss database established by this method can only fuzzy reflect the real situation and not for real to make fundamental explanation. In this paper, based on the study of the concept and steps of the MC simulation, we use computer simulation method, by using the MC simulation method in the "Program trading simulation system" developed by team to simulate the real programming trading and get the IT risk loss of data through its IT failure experiment, at the end of the article, on the effectiveness of the experimental data is verified. In this way, better overcome the deficiency of the traditional research method and solves the problem of lack of IT risk data in quantitative research. More empirically provides researchers with a set of simulation method are used to study the ideas and the process template.

A novel approach for evaluating the risk of health care failure modes.

PubMed

Chang, Dong Shang; Chung, Jenq Hann; Sun, Kuo Lung; Yang, Fu Chiang

2012-12-01

Failure mode and effects analysis (FMEA) can be employed to reduce medical errors by identifying the risk ranking of the health care failure modes and taking priority action for safety improvement. The purpose of this paper is to propose a novel approach of data analysis. The approach is to integrate FMEA and a mathematical tool-Data envelopment analysis (DEA) with "slack-based measure" (SBM), in the field of data analysis. The risk indexes (severity, occurrence, and detection) of FMEA are viewed as multiple inputs of DEA. The practicality and usefulness of the proposed approach is illustrated by one case of health care. Being a systematic approach for improving the service quality of health care, the approach can offer quantitative corrective information of risk indexes that thereafter reduce failure possibility. For safety improvement, these new targets of the risk indexes could be used for management by objectives. But FMEA cannot provide quantitative corrective information of risk indexes. The novel approach can surely overcome this chief shortcoming of FMEA. After combining DEA SBM model with FMEA, the two goals-increase of patient safety, medical cost reduction-can be together achieved.

Use of mechanistic simulations as a quantitative risk-ranking tool within the quality by design framework.

PubMed

Stocker, Elena; Toschkoff, Gregor; Sacher, Stephan; Khinast, Johannes G

2014-11-20

The purpose of this study is to evaluate the use of computer simulations for generating quantitative knowledge as a basis for risk ranking and mechanistic process understanding, as required by ICH Q9 on quality risk management systems. In this specific publication, the main focus is the demonstration of a risk assessment workflow, including a computer simulation for the generation of mechanistic understanding of active tablet coating in a pan coater. Process parameter screening studies are statistically planned under consideration of impacts on a potentially critical quality attribute, i.e., coating mass uniformity. Based on computer simulation data the process failure mode and effects analysis of the risk factors is performed. This results in a quantitative criticality assessment of process parameters and the risk priority evaluation of failure modes. The factor for a quantitative reassessment of the criticality and risk priority is the coefficient of variation, which represents the coating mass uniformity. The major conclusion drawn from this work is a successful demonstration of the integration of computer simulation in the risk management workflow leading to an objective and quantitative risk assessment. Copyright © 2014. Published by Elsevier B.V.

lazar: a modular predictive toxicology framework

PubMed Central

Maunz, Andreas; Gütlein, Martin; Rautenberg, Micha; Vorgrimmler, David; Gebele, Denis; Helma, Christoph

2013-01-01

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models. PMID:23761761

Toxicity Evaluation of Engineered Nanomaterials: Risk Evaluation Tools (Phase 3 Studies)

DTIC Science & Technology

2012-01-01

report. The second modeling approach was on quantitative structure activity relationships ( QSARs ). A manuscript entitled “Connecting the dots: Towards...expands rapidly. We proposed two types of mechanisms of toxic action supported by the nano- QSAR model , which collectively govern the toxicity of the...interpretative nano- QSAR model describing toxicity of 18 nano-metal oxides to a HaCaT cell line as a model for dermal exposure. In result, by the comparison of

Quantitative Risk Modeling of Fire on the International Space Station

NASA Technical Reports Server (NTRS)

Castillo, Theresa; Haught, Megan

2014-01-01

The International Space Station (ISS) Program has worked to prevent fire events and to mitigate their impacts should they occur. Hardware is designed to reduce sources of ignition, oxygen systems are designed to control leaking, flammable materials are prevented from flying to ISS whenever possible, the crew is trained in fire response, and fire response equipment improvements are sought out and funded. Fire prevention and mitigation are a top ISS Program priority - however, programmatic resources are limited; thus, risk trades are made to ensure an adequate level of safety is maintained onboard the ISS. In support of these risk trades, the ISS Probabilistic Risk Assessment (PRA) team has modeled the likelihood of fire occurring in the ISS pressurized cabin, a phenomenological event that has never before been probabilistically modeled in a microgravity environment. This paper will discuss the genesis of the ISS PRA fire model, its enhancement in collaboration with fire experts, and the results which have informed ISS programmatic decisions and will continue to be used throughout the life of the program.

Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates

EPA Science Inventory

Many different quantitative techniques have been developed to either assess Environmental Justice (EJ) issues or estimate exposure and dose for risk assessment. However, very few approaches have been applied to link EJ factors to exposure dose estimate and identify potential impa...

Health risks from exposure to Legionella in reclaimed water aerosols: Toilet flushing, spray irrigation, and cooling towers.

PubMed

Hamilton, Kerry A; Hamilton, Mark T; Johnson, William; Jjemba, Patrick; Bukhari, Zia; LeChevallier, Mark; Haas, Charles N

2018-05-01

The use of reclaimed water brings new challenges for the water industry in terms of maintaining water quality while increasing sustainability. Increased attention has been devoted to opportunistic pathogens, especially Legionella pneumophila, due to its growing importance as a portion of the waterborne disease burden in the United States. Infection occurs when a person inhales a mist containing Legionella bacteria. The top three uses for reclaimed water (cooling towers, spray irrigation, and toilet flushing) that generate aerosols were evaluated for Legionella health risks in reclaimed water using quantitative microbial risk assessment (QMRA). Risks are compared using data from nineteen United States reclaimed water utilities measured with culture-based methods, quantitative PCR (qPCR), and ethidium-monoazide-qPCR. Median toilet flushing annual infection risks exceeded 10 -4 considering multiple toilet types, while median clinical severity infection risks did not exceed this value. Sprinkler and cooling tower risks varied depending on meteorological conditions and operational characteristics such as drift eliminator performance. However, the greatest differences between risk scenarios were due to 1) the dose response model used (infection or clinical severity infection) 2) population at risk considered (residential or occupational) and 3) differences in laboratory analytical method. Theoretical setback distances necessary to achieve a median annual infection risk level of 10 -4 are proposed for spray irrigation and cooling towers. In both cooling tower and sprinkler cases, Legionella infection risks were non-trivial at potentially large setback distances, and indicate other simultaneous management practices could be needed to manage risks. The sensitivity analysis indicated that the most influential factors for variability in risks were the concentration of Legionella and aerosol partitioning and/or efficiency across all models, highlighting the importance of strategies to manage Legionella occurrence in reclaimed water. Copyright © 2018 Elsevier Ltd. All rights reserved.

Genetic Variants Associated With Quantitative Glucose Homeostasis Traits Translate to Type 2 Diabetes in Mexican Americans: The GUARDIAN (Genetics Underlying Diabetes in Hispanics) Consortium.

PubMed

Palmer, Nicholette D; Goodarzi, Mark O; Langefeld, Carl D; Wang, Nan; Guo, Xiuqing; Taylor, Kent D; Fingerlin, Tasha E; Norris, Jill M; Buchanan, Thomas A; Xiang, Anny H; Haritunians, Talin; Ziegler, Julie T; Williams, Adrienne H; Stefanovski, Darko; Cui, Jinrui; Mackay, Adrienne W; Henkin, Leora F; Bergman, Richard N; Gao, Xiaoyi; Gauderman, James; Varma, Rohit; Hanis, Craig L; Cox, Nancy J; Highland, Heather M; Below, Jennifer E; Williams, Amy L; Burtt, Noel P; Aguilar-Salinas, Carlos A; Huerta-Chagoya, Alicia; Gonzalez-Villalpando, Clicerio; Orozco, Lorena; Haiman, Christopher A; Tsai, Michael Y; Johnson, W Craig; Yao, Jie; Rasmussen-Torvik, Laura; Pankow, James; Snively, Beverly; Jackson, Rebecca D; Liu, Simin; Nadler, Jerry L; Kandeel, Fouad; Chen, Yii-Der I; Bowden, Donald W; Rich, Stephen S; Raffel, Leslie J; Rotter, Jerome I; Watanabe, Richard M; Wagenknecht, Lynne E

2015-05-01

Insulin sensitivity, insulin secretion, insulin clearance, and glucose effectiveness exhibit strong genetic components, although few studies have examined their genetic architecture or influence on type 2 diabetes (T2D) risk. We hypothesized that loci affecting variation in these quantitative traits influence T2D. We completed a multicohort genome-wide association study to search for loci influencing T2D-related quantitative traits in 4,176 Mexican Americans. Quantitative traits were measured by the frequently sampled intravenous glucose tolerance test (four cohorts) or euglycemic clamp (three cohorts), and random-effects models were used to test the association between loci and quantitative traits, adjusting for age, sex, and admixture proportions (Discovery). Analysis revealed a significant (P < 5.00 × 10(-8)) association at 11q14.3 (MTNR1B) with acute insulin response. Loci with P < 0.0001 among the quantitative traits were examined for translation to T2D risk in 6,463 T2D case and 9,232 control subjects of Mexican ancestry (Translation). Nonparametric meta-analysis of the Discovery and Translation cohorts identified significant associations at 6p24 (SLC35B3/TFAP2A) with glucose effectiveness/T2D, 11p15 (KCNQ1) with disposition index/T2D, and 6p22 (CDKAL1) and 11q14 (MTNR1B) with acute insulin response/T2D. These results suggest that T2D and insulin secretion and sensitivity have both shared and distinct genetic factors, potentially delineating genomic components of these quantitative traits that drive the risk for T2D. © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

A Short-Term Population Model of the Suicide Risk: The Case of Spain.

PubMed

De la Poza, Elena; Jódar, Lucas

2018-06-14

A relevant proportion of deaths by suicide have been attributed to other causes that produce the number of suicides remains hidden. The existence of a hidden number of cases is explained by the nature of the problem. Problems like this involve violence, and produce fear and social shame in victims' families. The existence of violence, fear and social shame experienced by victims favours a considerable number of suicides, identified as accidents or natural deaths. This paper proposes a short time discrete compartmental mathematical model to measure the suicidal risk for the case of Spain. The compartment model classifies and quantifies the amount of the Spanish population within the age intervals (16, 78) by their degree of suicide risk and their changes over time. Intercompartmental transits are due to the combination of quantitative and qualitative factors. Results are computed and simulations are performed to analyze the sensitivity of the model under uncertain coefficients.

A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents.

PubMed

Yu, Hongyang; Khan, Faisal; Veitch, Brian

2017-09-01

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation-based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source-to-source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry. © 2017 Society for Risk Analysis.

Engineering Risk Assessment of Space Thruster Challenge Problem

NASA Technical Reports Server (NTRS)

Mathias, Donovan L.; Mattenberger, Christopher J.; Go, Susie

2014-01-01

The Engineering Risk Assessment (ERA) team at NASA Ames Research Center utilizes dynamic models with linked physics-of-failure analyses to produce quantitative risk assessments of space exploration missions. This paper applies the ERA approach to the baseline and extended versions of the PSAM Space Thruster Challenge Problem, which investigates mission risk for a deep space ion propulsion system with time-varying thruster requirements and operations schedules. The dynamic mission is modeled using a combination of discrete and continuous-time reliability elements within the commercially available GoldSim software. Loss-of-mission (LOM) probability results are generated via Monte Carlo sampling performed by the integrated model. Model convergence studies are presented to illustrate the sensitivity of integrated LOM results to the number of Monte Carlo trials. A deterministic risk model was also built for the three baseline and extended missions using the Ames Reliability Tool (ART), and results are compared to the simulation results to evaluate the relative importance of mission dynamics. The ART model did a reasonable job of matching the simulation models for the baseline case, while a hybrid approach using offline dynamic models was required for the extended missions. This study highlighted that state-of-the-art techniques can adequately adapt to a range of dynamic problems.

Comparison study on qualitative and quantitative risk assessment methods for urban natural gas pipeline network.

PubMed

Han, Z Y; Weng, W G

2011-05-15

In this paper, a qualitative and a quantitative risk assessment methods for urban natural gas pipeline network are proposed. The qualitative method is comprised of an index system, which includes a causation index, an inherent risk index, a consequence index and their corresponding weights. The quantitative method consists of a probability assessment, a consequences analysis and a risk evaluation. The outcome of the qualitative method is a qualitative risk value, and for quantitative method the outcomes are individual risk and social risk. In comparison with previous research, the qualitative method proposed in this paper is particularly suitable for urban natural gas pipeline network, and the quantitative method takes different consequences of accidents into consideration, such as toxic gas diffusion, jet flame, fire ball combustion and UVCE. Two sample urban natural gas pipeline networks are used to demonstrate these two methods. It is indicated that both of the two methods can be applied to practical application, and the choice of the methods depends on the actual basic data of the gas pipelines and the precision requirements of risk assessment. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Climate change and dengue: a critical and systematic review of quantitative modelling approaches

PubMed Central

2014-01-01

Background Many studies have found associations between climatic conditions and dengue transmission. However, there is a debate about the future impacts of climate change on dengue transmission. This paper reviewed epidemiological evidence on the relationship between climate and dengue with a focus on quantitative methods for assessing the potential impacts of climate change on global dengue transmission. Methods A literature search was conducted in October 2012, using the electronic databases PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search focused on peer-reviewed journal articles published in English from January 1991 through October 2012. Results Sixteen studies met the inclusion criteria and most studies showed that the transmission of dengue is highly sensitive to climatic conditions, especially temperature, rainfall and relative humidity. Studies on the potential impacts of climate change on dengue indicate increased climatic suitability for transmission and an expansion of the geographic regions at risk during this century. A variety of quantitative modelling approaches were used in the studies. Several key methodological issues and current knowledge gaps were identified through this review. Conclusions It is important to assemble spatio-temporal patterns of dengue transmission compatible with long-term data on climate and other socio-ecological changes and this would advance projections of dengue risks associated with climate change. PMID:24669859

Quantitative prediction of drug side effects based on drug-related features.

PubMed

Niu, Yanqing; Zhang, Wen

2017-09-01

Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

Benchmark dose analysis via nonparametric regression modeling

PubMed Central

Piegorsch, Walter W.; Xiong, Hui; Bhattacharya, Rabi N.; Lin, Lizhen

2013-01-01

Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose-response modeling. It is a well-known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low-dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal-response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap-based confidence limits for the BMD. We explore the confidence limits’ small-sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty. PMID:23683057

Bone Health Monitoring in Astronauts: Recommended Use of Quantitative Computed Tomography [QCT] for Clinical and Operational Decisions

NASA Technical Reports Server (NTRS)

Sibonga, J. D.; Truskowski, P.

2010-01-01

This slide presentation reviews the concerns that astronauts in long duration flights might have a greater risk of bone fracture as they age than the general population. A panel of experts was convened to review the information and recommend mechanisms to monitor the health of bones in astronauts. The use of Quantitative Computed Tomography (QCT) scans for risk surveillance to detect the clinical trigger and to inform countermeasure evaluation is reviewed. An added benefit of QCT is that it facilitates an individualized estimation of bone strength by Finite Element Modeling (FEM), that can inform approaches for bone rehabilitation. The use of FEM is reviewed as a process that arrives at a composite number to estimate bone strength, because it integrates multiple factors.

What patient characteristics guide nurses' clinical judgement on pressure ulcer risk? A mixed methods study.

PubMed

Balzer, K; Kremer, L; Junghans, A; Halfens, R J G; Dassen, T; Kottner, J

2014-05-01

Nurses' clinical judgement plays a vital role in pressure ulcer risk assessment, but evidence is lacking which patient characteristics are important for nurses' perception of patients' risk exposure. To explore which patient characteristics nurses employ when assessing pressure ulcer risk without use of a risk assessment scale. Mixed methods design triangulating observational data from the control group of a quasi-experimental trial and data from semi-structured interviews with nurses. Two traumatological wards at a university hospital. Quantitative data: A consecutive sample of 106 patients matching the eligibility criteria (age ≥ 18 years, no pressure ulcers category ≥ 2 at admission and ≥ 5 days expected length of stay). Qualitative data: A purposive sample of 16 nurses. Quantitative data: Predictor variables for pressure ulcer risk were measured by study assistants at the bedside each second day. Concurrently, nurses documented their clinical judgement on patients' pressure ulcer risk by means of a 4-step global judgement scale. Bivariate correlations between predictor variables and nurses' risk estimates were established. Qualitative data: In interviews, nurses were asked to assess fictitious patients' pressure ulcer risk and to justify their risk estimates. Patient characteristics perceived as relevant for nurses' judements were thematically clustered. Triangulation: Firstly, predictors of nurses' risk estimates identified in bivariate analysis were cross-mapped with interview findings. Secondly, three models to predict nurses' risk estimates underwent multiple linear regression analysis. Nurses consider multiple patient characteristics for pressure ulcer risk assessment, but regard some conditions more important than others. Triangulation showed that these are measures reflecting patients' exposure to pressure or overall care dependency. Qualitative data furthermore indicate that nurses are likely to trade off risk-enhancing conditions against conditions perceived to be protective. Here, patients' mental capabilities like willingness to engage in one owns care seem to be particularly important. Due to missing information on these variables in the quantitative data, they could not be incorporated into triangulation. Nurses' clinical judgement draws on well-known aetiological factors, and tends to expand conditions covered by risk assessment scales. Patients' care dependency and self-care abilities seem to be core concepts for nurses' risk assessment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Risk of cervical injuries in mixed martial arts.

PubMed

Kochhar, T; Back, D L; Mann, B; Skinner, J

2005-07-01

Mixed martial arts have rapidly succeeded boxing as the world's most popular full contact sport, and the incidence of injury is recognised to be high. To assess qualitatively and quantitatively the potential risk for participants to sustain cervical spine and associated soft tissue injuries. Four commonly performed manoeuvres with possible risks to the cervical spine were analysed with respect to their kinematics, and biomechanical models were constructed. Motion analysis of two manoeuvres revealed strong correlations with rear end motor vehicle impact injuries, and kinematics of the remaining two suggested a strong risk of injury. Mathematical models of the biomechanics showed that the forces involved are of the same order as those involved in whiplash injuries and of the same magnitude as compression injuries of the cervical spine. This study shows that there is a significant risk of whiplash injuries in this sport, and there are no safety regulations to address these concerns.

Risk of cervical injuries in mixed martial arts

PubMed Central

Kochhar, T; Back, D; Mann, B; Skinner, J

2005-01-01

Background: Mixed martial arts have rapidly succeeded boxing as the world's most popular full contact sport, and the incidence of injury is recognised to be high. Objective: To assess qualitatively and quantitatively the potential risk for participants to sustain cervical spine and associated soft tissue injuries. Methods: Four commonly performed manoeuvres with possible risks to the cervical spine were analysed with respect to their kinematics, and biomechanical models were constructed. Results: Motion analysis of two manoeuvres revealed strong correlations with rear end motor vehicle impact injuries, and kinematics of the remaining two suggested a strong risk of injury. Mathematical models of the biomechanics showed that the forces involved are of the same order as those involved in whiplash injuries and of the same magnitude as compression injuries of the cervical spine. Conclusions: This study shows that there is a significant risk of whiplash injuries in this sport, and there are no safety regulations to address these concerns. PMID:15976168

A fault tree model to assess probability of contaminant discharge from shipwrecks.

PubMed

Landquist, H; Rosén, L; Lindhe, A; Norberg, T; Hassellöv, I-M; Lindgren, J F; Dahllöf, I

2014-11-15

Shipwrecks on the sea floor around the world may contain hazardous substances that can cause harm to the marine environment. Today there are no comprehensive methods for environmental risk assessment of shipwrecks, and thus there is poor support for decision-making on prioritization of mitigation measures. The purpose of this study was to develop a tool for quantitative risk estimation of potentially polluting shipwrecks, and in particular an estimation of the annual probability of hazardous substance discharge. The assessment of the probability of discharge is performed using fault tree analysis, facilitating quantification of the probability with respect to a set of identified hazardous events. This approach enables a structured assessment providing transparent uncertainty and sensitivity analyses. The model facilitates quantification of risk, quantification of the uncertainties in the risk calculation and identification of parameters to be investigated further in order to obtain a more reliable risk calculation. Copyright © 2014 Elsevier Ltd. All rights reserved.

A DOSIMETRIC ANALYSIS OF THE ACUTE BEHAVIORAL EFFECTS OF INHALED TOLUENE IN RATS

EPA Science Inventory

Knowledge of the appropriate metric of dose for a toxic chemical facilitates quantitative extrapolation of toxicity observed in the laboratory to the risk of adverse effects in the human population. Here we utilize a physiologically-based toxicokinetic (PBTK) model for toluene, a...

Designing a Quantitative Structure-Activity Relationship for the Intrinsic Metabolic Clearance of Environmentally Relevant Chemicals

EPA Science Inventory

Toxicokinetic models serve a vital role in risk assessment by bridging the gap between chemical exposure and potentially toxic endpoints. While intrinsic metabolic clearance rates have a strong impact on toxicokinetics, limited data is available for environmentally relevant chemi...

How Statisticians Speak Risk

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

Redus, K.S.

2007-07-01

The foundation of statistics deals with (a) how to measure and collect data and (b) how to identify models using estimates of statistical parameters derived from the data. Risk is a term used by the statistical community and those that employ statistics to express the results of a statistically based study. Statistical risk is represented as a probability that, for example, a statistical model is sufficient to describe a data set; but, risk is also interpreted as a measure of worth of one alternative when compared to another. The common thread of any risk-based problem is the combination of (a)more » the chance an event will occur, with (b) the value of the event. This paper presents an introduction to, and some examples of, statistical risk-based decision making from a quantitative, visual, and linguistic sense. This should help in understanding areas of radioactive waste management that can be suitably expressed using statistical risk and vice-versa. (authors)« less

Cancer risks after radiation exposure in middle age.

PubMed

Shuryak, Igor; Sachs, Rainer K; Brenner, David J

2010-11-03

Epidemiological data show that radiation exposure during childhood is associated with larger cancer risks compared with exposure at older ages. For exposures in adulthood, however, the relative risks of radiation-induced cancer in Japanese atomic bomb survivors generally do not decrease monotonically with increasing age of adult exposure. These observations are inconsistent with most standard models of radiation-induced cancer, which predict that relative risks decrease monotonically with increasing age at exposure, at all ages. We analyzed observed cancer risk patterns as a function of age at exposure in Japanese atomic bomb survivors by using a biologically based quantitative model of radiation carcinogenesis that incorporates both radiation induction of premalignant cells (initiation) and radiation-induced promotion of premalignant damage. This approach emphasizes the kinetics of radiation-induced initiation and promotion, and tracks the yields of premalignant cells before, during, shortly after, and long after radiation exposure. Radiation risks after exposure in younger individuals are dominated by initiation processes, whereas radiation risks after exposure at later ages are more influenced by promotion of preexisting premalignant cells. Thus, the cancer site-dependent balance between initiation and promotion determines the dependence of cancer risk on age at radiation exposure. For example, in terms of radiation induction of premalignant cells, a quantitative measure of the relative contribution of initiation vs promotion is 10-fold larger for breast cancer than for lung cancer. Reflecting this difference, radiation-induced breast cancer risks decrease with age at exposure at all ages, whereas radiation-induced lung cancer risks do not. For radiation exposure in middle age, most radiation-induced cancer risks do not, as often assumed, decrease with increasing age at exposure. This observation suggests that promotional processes in radiation carcinogenesis become increasingly important as the age at exposure increases. Radiation-induced cancer risks after exposure in middle age may be up to twice as high as previously estimated, which could have implications for occupational exposure and radiological imaging.

Integrated presentation of ecological risk from multiple stressors

NASA Astrophysics Data System (ADS)

Goussen, Benoit; Price, Oliver R.; Rendal, Cecilie; Ashauer, Roman

2016-10-01

Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.

Integrated presentation of ecological risk from multiple stressors.

PubMed

Goussen, Benoit; Price, Oliver R; Rendal, Cecilie; Ashauer, Roman

2016-10-26

Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.

Risk analysis for veterinary biologicals released into the environment.

PubMed

Silva, S V; Samagh, B S; Morley, R S

1995-12-01

All veterinary biologicals licensed in Canada must be shown to be pure, potent, safe and effective. A risk-based approach is used to evaluate the safety of all biologicals, whether produced by conventional methods or by molecular biological techniques. Traditionally, qualitative risk assessment methods have been used for this purpose. More recently, quantitative risk assessment has become available for complex issues. The quantitative risk assessment method uses "scenario tree analysis' to predict the likelihood of various outcomes and their respective impacts. The authors describe the quantitative risk assessment approach which is used within the broader context of risk analysis (i.e. risk assessment, risk management and risk communication) to develop recommendations for the field release of veterinary biologicals. The general regulatory framework for the licensing of veterinary biologicals in Canada is also presented.

Emerging systems biology approaches in nanotoxicology: Towards a mechanism-based understanding of nanomaterial hazard and risk

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

Costa, Pedro M.; Fadeel, Bengt, E-mail: Bengt.Fade

Engineered nanomaterials are being developed for a variety of technological applications. However, the increasing use of nanomaterials in society has led to concerns about their potential adverse effects on human health and the environment. During the first decade of nanotoxicological research, the realization has emerged that effective risk assessment of the multitudes of new nanomaterials would benefit from a comprehensive understanding of their toxicological mechanisms, which is difficult to achieve with traditional, low-throughput, single end-point oriented approaches. Therefore, systems biology approaches are being progressively applied within the nano(eco)toxicological sciences. This novel paradigm implies that the study of biological systems shouldmore » be integrative resulting in quantitative and predictive models of nanomaterial behaviour in a biological system. To this end, global ‘omics’ approaches with which to assess changes in genes, proteins, metabolites, etc. are deployed allowing for computational modelling of the biological effects of nanomaterials. Here, we highlight omics and systems biology studies in nanotoxicology, aiming towards the implementation of a systems nanotoxicology and mechanism-based risk assessment of nanomaterials. - Highlights: • Systems nanotoxicology is a multi-disciplinary approach to quantitative modelling. • Transcriptomics, proteomics and metabolomics remain the most common methods. • Global “omics” techniques should be coupled to computational modelling approaches. • The discovery of nano-specific toxicity pathways and biomarkers is a prioritized goal. • Overall, experimental nanosafety research must endeavour reproducibility and relevance.« less

An assessment of the risk of foreign animal disease introduction into the United States of America through garbage from Alaskan cruise ships.

PubMed

McElvaine, M D; McDowell, R M; Fite, R W; Miller, L

1993-12-01

The United States Department of Agriculture, Animal and Plant Health Inspection Service (USDA-APHIS) has been exploring methods of quantitative risk assessment to support decision-making, provide risk management options and identify research needs. With current changes in world trade, regulatory decisions must have a scientific basis which is transparent, consistent, documentable and defensible. These quantitative risk assessment methods are described in an accompanying paper in this issue. In the present article, the authors provide an illustration by presenting an application of these methods. Prior to proposing changes in regulations, USDA officials requested an assessment of the risk of introduction of foreign animal disease to the United States of America through garbage from Alaskan cruise ships. The risk assessment team used a combination of quantitative and qualitative methods to evaluate this question. Quantitative risk assessment methods were used to estimate the amount of materials of foreign origin being sent to Alaskan landfills. This application of quantitative risk assessment illustrates the flexibility of the methods in addressing specific questions. By applying these methods, specific areas were identified where more scientific information and research were needed. Even with limited information, the risk assessment provided APHIS management with a scientific basis for a regulatory decision.

Quantitative Risk Assessment of Antimicrobial-Resistant Foodborne Infections in Humans Due to Recombinant Bovine Somatotropin Usage in Dairy Cows.

PubMed

Singer, Randall S; Ruegg, Pamela L; Bauman, Dale E

2017-07-01

Recombinant bovine somatotropin (rbST) is a production-enhancing technology that allows the dairy industry to produce milk more efficiently. Concern has been raised that cows supplemented with rbST are at an increased risk of developing clinical mastitis, which would potentially increase the use of antimicrobial agents and increase human illnesses associated with antimicrobial-resistant bacterial pathogens delivered through the dairy beef supply. The purpose of this study was to conduct a quantitative risk assessment to estimate the potential increased risk of human infection with antimicrobial-resistant bacteria and subsequent adverse health outcomes as a result of rbST usage in dairy cattle. The quantitative risk assessment included the following steps: (i) release of antimicrobial-resistant organisms from the farm, (ii) exposure of humans via consumption of contaminated beef products, and (iii) consequence of the antimicrobial-resistant infection. The model focused on ceftiofur (parenteral and intramammary) and oxytetracycline (parenteral) treatment of clinical mastitis in dairy cattle and tracked the bacteria Campylobacter spp., Salmonella enterica subsp. enterica, and Escherichia coli in the gastrointestinal tract of the cow. Parameter estimates were developed to be maximum risk to overestimate the risk to humans. The excess number of cows in the U.S. dairy herd that were predicted to carry resistant bacteria at slaughter due to rbST administration was negligible. The total number of excess human illnesses caused by resistant bacteria due to rbST administration was also predicted to be negligible with all risks considerably less than one event per 1 billion people at risk per year for all bacteria. The results indicate a high probability that the use of rbST according to label instructions presents a negligible risk for increasing the number of human illnesses and subsequent adverse outcomes associated with antimicrobial-resistant Campylobacter, Salmonella, or E. coli .

The integrated effect of moderate exercise on coronary heart disease.

PubMed

Mathews, Marc J; Mathews, Edward H; Mathews, George E

Moderate exercise is associated with a lower risk for coronary heart disease (CHD). A suitable integrated model of the CHD pathogenetic pathways relevant to moderate exercise may help to elucidate this association. Such a model is currently not available in the literature. An integrated model of CHD was developed and used to investigate pathogenetic pathways of importance between exercise and CHD. Using biomarker relative-risk data, the pathogenetic effects are representable as measurable effects based on changes in biomarkers. The integrated model provides insight into higherorder interactions underlying the associations between CHD and moderate exercise. A novel 'connection graph' was developed, which simplifies these interactions. It quantitatively illustrates the relationship between moderate exercise and various serological biomarkers of CHD. The connection graph of moderate exercise elucidates all the possible integrated actions through which risk reduction may occur. An integrated model of CHD provides a summary of the effects of moderate exercise on CHD. It also shows the importance of each CHD pathway that moderate exercise influences. The CHD risk-reducing effects of exercise appear to be primarily driven by decreased inflammation and altered metabolism.

75 FR 25239 - Integrated Risk Information System (IRIS); Announcement of Availability of Literature Searches...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-07

... human health assessment program that evaluates quantitative and qualitative risk information on effects... quantitative and qualitative risk information on effects that may result from exposure to specific chemical...

The development of a 3D risk analysis method.

PubMed

I, Yet-Pole; Cheng, Te-Lung

2008-05-01

Much attention has been paid to the quantitative risk analysis (QRA) research in recent years due to more and more severe disasters that have happened in the process industries. Owing to its calculation complexity, very few software, such as SAFETI, can really make the risk presentation meet the practice requirements. However, the traditional risk presentation method, like the individual risk contour in SAFETI, is mainly based on the consequence analysis results of dispersion modeling, which usually assumes that the vapor cloud disperses over a constant ground roughness on a flat terrain with no obstructions and concentration fluctuations, which is quite different from the real situations of a chemical process plant. All these models usually over-predict the hazardous regions in order to maintain their conservativeness, which also increases the uncertainty of the simulation results. On the other hand, a more rigorous model such as the computational fluid dynamics (CFD) model can resolve the previous limitations; however, it cannot resolve the complexity of risk calculations. In this research, a conceptual three-dimensional (3D) risk calculation method was proposed via the combination of results of a series of CFD simulations with some post-processing procedures to obtain the 3D individual risk iso-surfaces. It is believed that such technique will not only be limited to risk analysis at ground level, but also be extended into aerial, submarine, or space risk analyses in the near future.

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.

QMRA for Drinking Water: 1. Revisiting the Mathematical Structure of Single-Hit Dose-Response Models.

PubMed

Nilsen, Vegard; Wyller, John

2016-01-01

Dose-response models are essential to quantitative microbial risk assessment (QMRA), providing a link between levels of human exposure to pathogens and the probability of negative health outcomes. In drinking water studies, the class of semi-mechanistic models known as single-hit models, such as the exponential and the exact beta-Poisson, has seen widespread use. In this work, an attempt is made to carefully develop the general mathematical single-hit framework while explicitly accounting for variation in (1) host susceptibility and (2) pathogen infectivity. This allows a precise interpretation of the so-called single-hit probability and precise identification of a set of statistical independence assumptions that are sufficient to arrive at single-hit models. Further analysis of the model framework is facilitated by formulating the single-hit models compactly using probability generating and moment generating functions. Among the more practically relevant conclusions drawn are: (1) for any dose distribution, variation in host susceptibility always reduces the single-hit risk compared to a constant host susceptibility (assuming equal mean susceptibilities), (2) the model-consistent representation of complete host immunity is formally demonstrated to be a simple scaling of the response, (3) the model-consistent expression for the total risk from repeated exposures deviates (gives lower risk) from the conventional expression used in applications, and (4) a model-consistent expression for the mean per-exposure dose that produces the correct total risk from repeated exposures is developed. © 2016 Society for Risk Analysis.

Job stress, fatigue, and job dissatisfaction in Dutch lorry drivers: towards an occupation specific model of job demands and control

PubMed Central

de Croon, E M; Blonk, R; de Zwart, B C H; Frings-Dresen, M; Broersen, J

2002-01-01

Objectives: Building on Karasek's model of job demands and control (JD-C model), this study examined the effects of job control, quantitative workload, and two occupation specific job demands (physical demands and supervisor demands) on fatigue and job dissatisfaction in Dutch lorry drivers. Methods: From 1181 lorry drivers (adjusted response 63%) self reported information was gathered by questionnaire on the independent variables (job control, quantitative workload, physical demands, and supervisor demands) and the dependent variables (fatigue and job dissatisfaction). Stepwise multiple regression analyses were performed to examine the main effects of job demands and job control and the interaction effect between job control and job demands on fatigue and job dissatisfaction. Results: The inclusion of physical and supervisor demands in the JD-C model explained a significant amount of variance in fatigue (3%) and job dissatisfaction (7%) over and above job control and quantitative workload. Moreover, in accordance with Karasek's interaction hypothesis, job control buffered the positive relation between quantitative workload and job dissatisfaction. Conclusions: Despite methodological limitations, the results suggest that the inclusion of (occupation) specific job control and job demand measures is a fruitful elaboration of the JD-C model. The occupation specific JD-C model gives occupational stress researchers better insight into the relation between the psychosocial work environment and wellbeing. Moreover, the occupation specific JD-C model may give practitioners more concrete and useful information about risk factors in the psychosocial work environment. Therefore, this model may provide points of departure for effective stress reducing interventions at work. PMID:12040108

Job stress, fatigue, and job dissatisfaction in Dutch lorry drivers: towards an occupation specific model of job demands and control.

PubMed

de Croon, E M; Blonk, R W B; de Zwart, B C H; Frings-Dresen, M H W; Broersen, J P J

2002-06-01

Building on Karasek's model of job demands and control (JD-C model), this study examined the effects of job control, quantitative workload, and two occupation specific job demands (physical demands and supervisor demands) on fatigue and job dissatisfaction in Dutch lorry drivers. From 1181 lorry drivers (adjusted response 63%) self reported information was gathered by questionnaire on the independent variables (job control, quantitative workload, physical demands, and supervisor demands) and the dependent variables (fatigue and job dissatisfaction). Stepwise multiple regression analyses were performed to examine the main effects of job demands and job control and the interaction effect between job control and job demands on fatigue and job dissatisfaction. The inclusion of physical and supervisor demands in the JD-C model explained a significant amount of variance in fatigue (3%) and job dissatisfaction (7%) over and above job control and quantitative workload. Moreover, in accordance with Karasek's interaction hypothesis, job control buffered the positive relation between quantitative workload and job dissatisfaction. Despite methodological limitations, the results suggest that the inclusion of (occupation) specific job control and job demand measures is a fruitful elaboration of the JD-C model. The occupation specific JD-C model gives occupational stress researchers better insight into the relation between the psychosocial work environment and wellbeing. Moreover, the occupation specific JD-C model may give practitioners more concrete and useful information about risk factors in the psychosocial work environment. Therefore, this model may provide points of departure for effective stress reducing interventions at work.

DIFFERENTIAL EFFECTS OF CARBARYL IN BRAIN ACONITASE ACTIVITY IN SPONTANEOUSLY HYPERTENSIVE (SHR) AND WISTAR-KYOTO (WKY) RATS.

EPA Science Inventory

Animal models of susceptibility are crucial for quantitative human health risk assessment. Spontaneously hypertensive rats (SHR) have long been used in studies on the etiology and mechanisms of hypertension and are known to be prone to oxidative stress. Previous studies indica...

Integrating watershed- and farm-scale modeling framework for targeting critical source areas while maintaining farm economic viability

USDA-ARS?s Scientific Manuscript database

Quantitative risk assessments of pollution and data related to the effectiveness of mitigating best management practices (BMPs) are important aspects of nonpoint source (NPS) pollution control efforts, particularly those driven by specific water quality objectives and by measurable improvement goals...

Reliability and safety, and the risk of construction damage in mining areas

NASA Astrophysics Data System (ADS)

Skrzypczak, Izabela; Kogut, Janusz P.; Kokoszka, Wanda; Oleniacz, Grzegorz

2018-04-01

This article concerns the reliability and safety of building structures in mining areas, with a particular emphasis on the quantitative risk analysis of buildings. The issues of threat assessment and risk estimation, in the design of facilities in mining exploitation areas, are presented here, indicating the difficulties and ambiguities associated with their quantification and quantitative analysis. This article presents the concept of quantitative risk assessment of the impact of mining exploitation, in accordance with ISO 13824 [1]. The risk analysis is illustrated through an example of a construction located within an area affected by mining exploitation.

Electric vehicle (EV) storage supply chain risk and the energy market: A micro and macroeconomic risk management approach

NASA Astrophysics Data System (ADS)

Aguilar, Susanna D.

As a cost effective storage technology for renewable energy sources, Electric Vehicles can be integrated into energy grids. Integration must be optimized to ascertain that renewable energy is available through storage when demand exists so that cost of electricity is minimized. Optimization models can address economic risks associated with the EV supply chain- particularly the volatility in availability and cost of critical materials used in the manufacturing of EV motors and batteries. Supply chain risk can reflect itself in a shortage of storage, which can increase the price of electricity. We propose a micro-and macroeconomic framework for managing supply chain risk through utilization of a cost optimization model in combination with risk management strategies at the microeconomic and macroeconomic level. The study demonstrates how risk from the EVs vehicle critical material supply chain affects manufacturers, smart grid performance, and energy markets qualitatively and quantitatively. Our results illustrate how risk in the EV supply chain affects EV availability and the cost of ancillary services, and how EV critical material supply chain risk can be mitigated through managerial strategies and policy.

A Quantitative Risk-Benefit Analysis of Prophylactic Surgery Prior to Extended-Duration Spaceflight

NASA Technical Reports Server (NTRS)

Carroll, Danielle; Reyes, David; Kerstman, Eric; Walton, Marlei; Antonsen, Erik

2017-01-01

INTRODUCTION: Among otherwise healthy astronauts undertaking deep space missions, the risks for acute appendicitis (AA) and cholecystitis (AC) are not zero. If these conditions were to occur during spaceflight they may require surgery for definitive care. The proposed study quantifies and compares the risks of developing de novo AA and AC in-flight to the surgical risks of prophylactic laparoscopic appendectomy (LA) and cholecystectomy (LC) using NASA's Integrated Medical Model (IMM). METHODS: The IMM is a Monte Carlo simulation that forecasts medical events during spaceflight missions and estimates the impact of these medical events on crew health. In this study, four Design Reference Missions (DRMs) were created to assess the probability of an astronaut developing in-flight small-bowel obstruction (SBO) following prophylactic 1) LA, 2) LC, 3) LA and LC, or 4) neither surgery (SR# S-20160407-351). Model inputs were drawn from a large, population-based 2011 Swedish study that examined the incidence and risks of post-operative SBO over a 5-year follow-up period. The study group included 1,152 patients who underwent LA, and 16,371 who underwent LC. RESULTS: Preliminary results indicate that prophylactic LA may yield higher mission risks than the control DRM. Complete analyses are pending and will be subsequently available. DISCUSSION: The risk versus benefits of prophylactic surgery in astronauts to decrease the probability of acute surgical events during spaceflight has only been qualitatively examined in prior studies. Within the assumptions and limitations of the IMM, this work provides the first quantitative guidance that has previously been lacking to this important question for future deep space exploration missions.

Risk-Informed Safety Assurance and Probabilistic Assessment of Mission-Critical Software-Intensive Systems

NASA Technical Reports Server (NTRS)

Guarro, Sergio B.

2010-01-01

This report validates and documents the detailed features and practical application of the framework for software intensive digital systems risk assessment and risk-informed safety assurance presented in the NASA PRA Procedures Guide for Managers and Practitioner. This framework, called herein the "Context-based Software Risk Model" (CSRM), enables the assessment of the contribution of software and software-intensive digital systems to overall system risk, in a manner which is entirely compatible and integrated with the format of a "standard" Probabilistic Risk Assessment (PRA), as currently documented and applied for NASA missions and applications. The CSRM also provides a risk-informed path and criteria for conducting organized and systematic digital system and software testing so that, within this risk-informed paradigm, the achievement of a quantitatively defined level of safety and mission success assurance may be targeted and demonstrated. The framework is based on the concept of context-dependent software risk scenarios and on the modeling of such scenarios via the use of traditional PRA techniques - i.e., event trees and fault trees - in combination with more advanced modeling devices such as the Dynamic Flowgraph Methodology (DFM) or other dynamic logic-modeling representations. The scenarios can be synthesized and quantified in a conditional logic and probabilistic formulation. The application of the CSRM method documented in this report refers to the MiniAERCam system designed and developed by the NASA Johnson Space Center.

Building a Database for a Quantitative Model

NASA Technical Reports Server (NTRS)

Kahn, C. Joseph; Kleinhammer, Roger

2014-01-01

A database can greatly benefit a quantitative analysis. The defining characteristic of a quantitative risk, or reliability, model is the use of failure estimate data. Models can easily contain a thousand Basic Events, relying on hundreds of individual data sources. Obviously, entering so much data by hand will eventually lead to errors. Not so obviously entering data this way does not aid linking the Basic Events to the data sources. The best way to organize large amounts of data on a computer is with a database. But a model does not require a large, enterprise-level database with dedicated developers and administrators. A database built in Excel can be quite sufficient. A simple spreadsheet database can link every Basic Event to the individual data source selected for them. This database can also contain the manipulations appropriate for how the data is used in the model. These manipulations include stressing factors based on use and maintenance cycles, dormancy, unique failure modes, the modeling of multiple items as a single "Super component" Basic Event, and Bayesian Updating based on flight and testing experience. A simple, unique metadata field in both the model and database provides a link from any Basic Event in the model to its data source and all relevant calculations. The credibility for the entire model often rests on the credibility and traceability of the data.

Skin sensitisation quantitative risk assessment (QRA) based on aggregate dermal exposure to methylisothiazolinone in personal care and household cleaning products.

PubMed

Ezendam, J; Bokkers, B G H; Bil, W; Delmaar, J E

2018-02-01

Contact allergy to preservatives is an important public health problem. Ideally, new substances should be evaluated for the risk on skin sensitisation before market entry, for example by using a quantitative risk assessment (QRA) as developed for fragrances. As a proof-of-concept, this QRA was applied to the preservative methylisothiazolinone (MI), a common cause of contact allergy. MI is used in different consumer products, including personal care products (PCPs) and household cleaning products (HCPs). Aggregate exposure to MI in PCPs and HCPs was therefore assessed with the Probabilistic Aggregated Consumer Exposure Model (PACEM). Two exposure scenarios were evaluated: scenario 1 calculated aggregate exposure on actual MI product concentrations before the restricted use in PCPs and scenario 2 calculated aggregate exposure using the restrictions for MI in PCPs. The QRA for MI showed that in scenarios 1 and 2, the proportion of the population at risk for skin sensitisation is 0.7% and 0.5%, respectively. The restricted use of MI in PCPs does not seem very effective in lowering the risk on skin sensitization. To conclude, it is important to consider aggregate exposure from the most important consumer products into consideration in the risk assessment. Copyright © 2018 Elsevier Ltd. All rights reserved.

17 CFR 240.15c3-1f - Optional market and credit risk requirements for OTC derivatives dealers (Appendix F to 17 CFR...

Code of Federal Regulations, 2013 CFR

2013-04-01

... observations cannot be less than six months. Historical data sets must be updated at least every three months... quantitative aspects of the model which at a minimum must adhere to the criteria set forth in paragraph (e) of..., a description of how its own theoretical pricing model contains the minimum pricing factors set...

Bayesian data assimilation provides rapid decision support for vector-borne diseases.

PubMed

Jewell, Chris P; Brown, Richard G

2015-07-06

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Quantitative systems toxicology

PubMed Central

Bloomingdale, Peter; Housand, Conrad; Apgar, Joshua F.; Millard, Bjorn L.; Mager, Donald E.; Burke, John M.; Shah, Dhaval K.

2017-01-01

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety. PMID:29308440

The Research on Safety Management Information System of Railway Passenger Based on Risk Management Theory

NASA Astrophysics Data System (ADS)

Zhu, Wenmin; Jia, Yuanhua

2018-01-01

Based on the risk management theory and the PDCA cycle model, requirements of the railway passenger transport safety production is analyzed, and the establishment of the security risk assessment team is proposed to manage risk by FTA with Delphi from both qualitative and quantitative aspects. The safety production committee is also established to accomplish performance appraisal, which is for further ensuring the correctness of risk management results, optimizing the safety management business processes and improving risk management capabilities. The basic framework and risk information database of risk management information system of railway passenger transport safety are designed by Ajax, Web Services and SQL technologies. The system realizes functions about risk management, performance appraisal and data management, and provides an efficient and convenient information management platform for railway passenger safety manager.

Retrospective and current risks of mercury to panthers in the Florida Everglades.

PubMed

Barron, Mace G; Duvall, Stephanie E; Barron, Kyle J

2004-04-01

Florida panthers are an endangered species inhabiting south Florida. Hg has been suggested as a causative factor for low populations and some reported panther deaths, but a quantitative assessment of risks has never been performed. This study quantitatively evaluated retrospective (pre-1992) and current (2002) risks of chronic dietary Hg exposures to panthers in the Florida Everglades. A probabilistic assessment of Hg risks was performed using a dietary exposure model and Latin Hypercube sampling that incorporated the variability and uncertainty in ingestion rate, diet, body weight, and mercury exposure of panthers. Hazard quotients (HQs) for retrospective risks ranged from less than 0.1-20, with a 46% probability of exceeding chronic dietary thresholds for methylmercury. Retrospective risks of developing clinical symptoms, including ataxia and convulsions, had an HQ range of <0.1-5.4 with a 17% probability of exceeding an HQ of 1. Current risks were substantially lower (4% probability of exceedences; HQ range <0.1-3.5) because of an estimated 70-90% decline in Hg exposure to panthers over the last decade. Under worst case conditions of panthers consuming only raccoons from the most contaminated area of the Everglades, current risks of developing clinical symptoms that may lead to death was 4.6%. Current risks of mercury poisoning of panthers with a diversified diet was 0.1% (HQ range of <0.1-1.4). The results of this assessment indicate that past Hg exposures likely adversely affected panthers in the Everglades, but current risks of Hg are low.

Cheese Microbial Risk Assessments — A Review

PubMed Central

Choi, Kyoung-Hee; Lee, Heeyoung; Lee, Soomin; Kim, Sejeong; Yoon, Yohan

2016-01-01

Cheese is generally considered a safe and nutritious food, but foodborne illnesses linked to cheese consumption have occurred in many countries. Several microbial risk assessments related to Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli infections, causing cheese-related foodborne illnesses, have been conducted. Although the assessments of microbial risk in soft and low moisture cheeses such as semi-hard and hard cheeses have been accomplished, it has been more focused on the correlations between pathogenic bacteria and soft cheese, because cheese-associated foodborne illnesses have been attributed to the consumption of soft cheeses. As a part of this microbial risk assessment, predictive models have been developed to describe the relationship between several factors (pH, Aw, starter culture, and time) and the fates of foodborne pathogens in cheese. Predictions from these studies have been used for microbial risk assessment as a part of exposure assessment. These microbial risk assessments have identified that risk increased in cheese with high moisture content, especially for raw milk cheese, but the risk can be reduced by preharvest and postharvest preventions. For accurate quantitative microbial risk assessment, more data including interventions such as curd cooking conditions (temperature and time) and ripening period should be available for predictive models developed with cheese, cheese consumption amounts and cheese intake frequency data as well as more dose-response models. PMID:26950859

Quantitative weight of evidence assessment of risk to honeybee colonies from use of imidacloprid, clothianidin, and thiamethoxam as seed treatments: a postscript.

PubMed

Solomon, Keith R; Stephenson, Gladys L

2017-01-01

This paper is a postscript to the four companion papers in this issue of the Journal (Solomon and Stephenson 2017a , 2017b ; Stephenson and Solomon 2017a , 2017b ). The first paper in the series described the conceptual model and the methods of the QWoE process. The other three papers described the application of the QWoE process to studies on imidacloprid (IMI), clothianidin (CTD), and thiamethoxam (TMX). This postscript was written to summarize the utility of the methods used in the quantitative weight of evidence (QWoE), the overall relevance of the results, and the environmental implications of the findings. Hopefully, this will be helpful to others who wish to conduct QWoEs and use these methods in assessment of risks.

12 CFR 217.173 - Disclosures by certain advanced approaches Board-regulated institutions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... total capital within the group. Quantitative disclosures (d) The aggregate amount of surplus capital of... conditions of the main features of all regulatory capital instruments. Quantitative disclosures (b) The... current and future activities. Quantitative disclosures (b) Risk-weighted assets for credit risk from: (1...

12 CFR 324.173 - Disclosures by certain advanced approaches FDIC-supervised institutions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... total capital within the group. Quantitative disclosures (d) The aggregate amount of surplus capital of... conditions of the main features of all regulatory capital instruments. Quantitative disclosures (b) The... current and future activities. Quantitative disclosures (b) Risk-weighted assets for credit risk from:(1...

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.

A clustering approach to segmenting users of internet-based risk calculators.

PubMed

Harle, C A; Downs, J S; Padman, R

2011-01-01

Risk calculators are widely available Internet applications that deliver quantitative health risk estimates to consumers. Although these tools are known to have varying effects on risk perceptions, little is known about who will be more likely to accept objective risk estimates. To identify clusters of online health consumers that help explain variation in individual improvement in risk perceptions from web-based quantitative disease risk information. A secondary analysis was performed on data collected in a field experiment that measured people's pre-diabetes risk perceptions before and after visiting a realistic health promotion website that provided quantitative risk information. K-means clustering was performed on numerous candidate variable sets, and the different segmentations were evaluated based on between-cluster variation in risk perception improvement. Variation in responses to risk information was best explained by clustering on pre-intervention absolute pre-diabetes risk perceptions and an objective estimate of personal risk. Members of a high-risk overestimater cluster showed large improvements in their risk perceptions, but clusters of both moderate-risk and high-risk underestimaters were much more muted in improving their optimistically biased perceptions. Cluster analysis provided a unique approach for segmenting health consumers and predicting their acceptance of quantitative disease risk information. These clusters suggest that health consumers were very responsive to good news, but tended not to incorporate bad news into their self-perceptions much. These findings help to quantify variation among online health consumers and may inform the targeted marketing of and improvements to risk communication tools on the Internet.

Health impact assessment – A survey on quantifying tools

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

Fehr, Rainer, E-mail: rainer.fehr@uni-bielefeld.de; Mekel, Odile C.L., E-mail: odile.mekel@lzg.nrw.de; Fintan Hurley, J., E-mail: fintan.hurley@iom-world.org

Integrating human health into prospective impact assessments is known to be challenging. This is true for both approaches: dedicated health impact assessments (HIA) as well as inclusion of health into more general impact assessments. Acknowledging the full range of participatory, qualitative, and quantitative approaches, this study focuses on the latter, especially on computational tools for quantitative health modelling. We conducted a survey among tool developers concerning the status quo of development and availability of such tools; experiences made with model usage in real-life situations; and priorities for further development. Responding toolmaker groups described 17 such tools, most of them beingmore » maintained and reported as ready for use and covering a wide range of topics, including risk & protective factors, exposures, policies, and health outcomes. In recent years, existing models have been improved and were applied in new ways, and completely new models emerged. There was high agreement among respondents on the need to further develop methods for assessment of inequalities and uncertainty. The contribution of quantitative modeling to health foresight would benefit from building joint strategies of further tool development, improving the visibility of quantitative tools and methods, and engaging continuously with actual and potential users. - Highlights: • A survey investigated computational tools for health impact quantification. • Formal evaluation of such tools has been rare. • Handling inequalities and uncertainties are priority areas for further development. • Health foresight would benefit from tool developers and users forming a community. • Joint development strategies across computational tools are needed.« less

Hazard Screening Methods for Nanomaterials: A Comparative Study

PubMed Central

Murphy, Finbarr; Mullins, Martin; Furxhi, Irini; Costa, Anna L.; Simeone, Felice C.

2018-01-01

Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs—TiO2, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework. PMID:29495342

Landslide risk models for decision making.

PubMed

Bonachea, Jaime; Remondo, Juan; de Terán, José Ramón Díaz; González-Díez, Alberto; Cendrero, Antonio

2009-11-01

This contribution presents a quantitative procedure for landslide risk analysis and zoning considering hazard, exposure (or value of elements at risk), and vulnerability. The method provides the means to obtain landslide risk models (expressing expected damage due to landslides on material elements and economic activities in monetary terms, according to different scenarios and periods) useful to identify areas where mitigation efforts will be most cost effective. It allows identifying priority areas for the implementation of actions to reduce vulnerability (elements) or hazard (processes). The procedure proposed can also be used as a preventive tool, through its application to strategic environmental impact analysis (SEIA) of land-use plans. The underlying hypothesis is that reliable predictions about hazard and risk can be made using models based on a detailed analysis of past landslide occurrences in connection with conditioning factors and data on past damage. The results show that the approach proposed and the hypothesis formulated are essentially correct, providing estimates of the order of magnitude of expected losses for a given time period. Uncertainties, strengths, and shortcomings of the procedure and results obtained are discussed and potential lines of research to improve the models are indicated. Finally, comments and suggestions are provided to generalize this type of analysis.

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.

A model-based analysis of decision making under risk in obsessive-compulsive and hoarding disorders.

PubMed

Aranovich, Gabriel J; Cavagnaro, Daniel R; Pitt, Mark A; Myung, Jay I; Mathews, Carol A

2017-07-01

Attitudes towards risk are highly consequential in clinical disorders thought to be prone to "risky behavior", such as substance dependence, as well as those commonly associated with excessive risk aversion, such as obsessive-compulsive disorder (OCD) and hoarding disorder (HD). Moreover, it has recently been suggested that attitudes towards risk may serve as a behavioral biomarker for OCD. We investigated the risk preferences of participants with OCD and HD using a novel adaptive task and a quantitative model from behavioral economics that decomposes risk preferences into outcome sensitivity and probability sensitivity. Contrary to expectation, compared to healthy controls, participants with OCD and HD exhibited less outcome sensitivity, implying less risk aversion in the standard economic framework. In addition, risk attitudes were strongly correlated with depression, hoarding, and compulsion scores, while compulsion (hoarding) scores were associated with more (less) "rational" risk preferences. These results demonstrate how fundamental attitudes towards risk relate to specific psychopathology and thereby contribute to our understanding of the cognitive manifestations of mental disorders. In addition, our findings indicate that the conclusion made in recent work that decision making under risk is unaltered in OCD is premature. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India

NASA Astrophysics Data System (ADS)

Jaiswal, P.; van Westen, C. J.; Jetten, V.

2011-06-01

A quantitative procedure for estimating landslide risk to life and property is presented and applied in a mountainous area in the Nilgiri hills of southern India. Risk is estimated for elements at risk located in both initiation zones and run-out paths of potential landslides. Loss of life is expressed as individual risk and as societal risk using F-N curves, whereas the direct loss of properties is expressed in monetary terms. An inventory of 1084 landslides was prepared from historical records available for the period between 1987 and 2009. A substantially complete inventory was obtained for landslides on cut slopes (1042 landslides), while for natural slopes information on only 42 landslides was available. Most landslides were shallow translational debris slides and debris flowslides triggered by rainfall. On natural slopes most landslides occurred as first-time failures. For landslide hazard assessment the following information was derived: (1) landslides on natural slopes grouped into three landslide magnitude classes, based on landslide volumes, (2) the number of future landslides on natural slopes, obtained by establishing a relationship between the number of landslides on natural slopes and cut slopes for different return periods using a Gumbel distribution model, (3) landslide susceptible zones, obtained using a logistic regression model, and (4) distribution of landslides in the susceptible zones, obtained from the model fitting performance (success rate curve). The run-out distance of landslides was assessed empirically using landslide volumes, and the vulnerability of elements at risk was subjectively assessed based on limited historic incidents. Direct specific risk was estimated individually for tea/coffee and horticulture plantations, transport infrastructures, buildings, and people both in initiation and run-out areas. Risks were calculated by considering the minimum, average, and maximum landslide volumes in each magnitude class and the corresponding minimum, average, and maximum run-out distances and vulnerability values, thus obtaining a range of risk values per return period. The results indicate that the total annual minimum, average, and maximum losses are about US 44 000, US 136 000 and US 268 000, respectively. The maximum risk to population varies from 2.1 × 10-1 for one or more lives lost to 6.0 × 10-2 yr-1 for 100 or more lives lost. The obtained results will provide a basis for planning risk reduction strategies in the Nilgiri area.

Associating Changes in the Immune System with Clinical Diseases for Interpretation in Risk Assessment

PubMed Central

Germolec, Dori R.; Luebke, Robert W.; Johnson, Victor J.

2016-01-01

This overview is an update of the unit originally published in 2004. While the basic tenants of immunotoxicity have not changed in the past 10 years, several publications have explored the application of immunotoxicological data to the risk assessment process. Therefore, the goal of this unit is still to highlight relationships between xenobiotic-induced immunosuppression and risk of clinical diseases progression. In immunotoxicology, this may require development of models to equate moderate changes in markers of immune functions to potential changes in incidence or severity of infectious diseases. For most xenobiotics, exposure levels and disease incidence data are rarely available and safe exposure levels must be estimated based on observations from experimental models or human biomarker studies. Thus, it is important to establish a scientifically sound framework that allows accurate and quantitative interpretation of experimental or biomarker data in the risk assessment process. PMID:26828330

Associating Changes in the Immune System with Clinical Diseases for Interpretation in Risk Assessment.

PubMed

DeWitt, Jamie C; Germolec, Dori R; Luebke, Robert W; Johnson, Victor J

2016-02-01

This overview is an update of the unit originally published in 2004. While the basic tenets of immunotoxicity have not changed in the past 10 years, several publications have explored the application of immunotoxicological data to the risk assessment process. Therefore, the goal of this unit is still to highlight relationships between xenobiotic-induced immunosuppression and risk of clinical diseases progression. In immunotoxicology, this may require development of models to equate moderate changes in markers of immune functions to potential changes in incidence or severity of infectious diseases. For most xenobiotics, exposure levels and disease incidence data are rarely available, and safe exposure levels must be estimated based on observations from experimental models or human biomarker studies. Thus, it is important to establish a scientifically sound framework that allows accurate and quantitative interpretation of experimental or biomarker data in the risk assessment process. Copyright © 2016 John Wiley & Sons, Inc.

Principals' Leadership Behaviors as Perceived by Teachers in At-Risk Middle Schools

ERIC Educational Resources Information Center

Johnson, R. Anthony

2011-01-01

A need for greater understanding of teachers' (N = 530) perceptions of the leadership behaviors of principals in Title I middle schools (n = 13) is prevalent exists. The researcher used the "Audit of Principal Effectiveness" survey to collect data. The researcher also used Hierarchical Linear Modeling as the quantitative analysis.…

Acceptance Factors Influencing Adoption of National Institute of Standards and Technology Information Security Standards: A Quantitative Study

ERIC Educational Resources Information Center

Kiriakou, Charles M.

2012-01-01

Adoption of a comprehensive information security governance model and security controls is the best option organizations may have to protect their information assets and comply with regulatory requirements. Understanding acceptance factors of the National Institute of Standards and Technology (NIST) Risk Management Framework (RMF) comprehensive…

IT Operational Risk Measurement Model Based on Internal Loss Data of Banks

NASA Astrophysics Data System (ADS)

Hao, Xiaoling

Business operation of banks relies increasingly on information technology (IT) and the most important role of IT is to guarantee the operational continuity of business process. Therefore, IT Risk management efforts need to be seen from the perspective of operational continuity. Traditional IT risk studies focused on IT asset-based risk analysis and risk-matrix based qualitative risk evaluation. In practice, IT risk management practices of banking industry are still limited to the IT department and aren't integrated into business risk management, which causes the two departments to work in isolation. This paper presents an improved methodology for dealing with IT operational risk. It adopts quantitative measurement method, based on the internal business loss data about IT events, and uses Monte Carlo simulation to predict the potential losses. We establish the correlation between the IT resources and business processes to make sure risk management of IT and business can work synergistically.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age.

PubMed

Baños, Núria; Perez-Moreno, Alvaro; Migliorelli, Federico; Triginer, Laura; Cobo, Teresa; Bonet-Carne, Elisenda; Gratacos, Eduard; Palacio, Montse

2017-01-01

Quantitative texture analysis has been proposed to extract robust features from the ultrasound image to detect subtle changes in the textures of the images. The aim of this study was to evaluate the feasibility of quantitative cervical texture analysis to assess cervical tissue changes throughout pregnancy. This was a cross-sectional study including singleton pregnancies between 20.0 and 41.6 weeks of gestation from women who delivered at term. Cervical length was measured, and a selected region of interest in the cervix was delineated. A model to predict gestational age based on features extracted from cervical images was developed following three steps: data splitting, feature transformation, and regression model computation. Seven hundred images, 30 per gestational week, were included for analysis. There was a strong correlation between the gestational age at which the images were obtained and the estimated gestational age by quantitative analysis of the cervical texture (R = 0.88). This study provides evidence that quantitative analysis of cervical texture can extract features from cervical ultrasound images which correlate with gestational age. Further research is needed to evaluate its applicability as a biomarker of the risk of spontaneous preterm birth, as well as its role in cervical assessment in other clinical situations in which cervical evaluation might be relevant. © 2016 S. Karger AG, Basel.

A quantitative microbial risk assessment for center pivot irrigation of dairy wastewaters

USDA-ARS?s Scientific Manuscript database

In the western United States where livestock wastewaters are commonly land applied, there are concerns over individuals being exposed to airborne pathogens. In response, a quantitative microbial risk assessment (QMRA) was performed to estimate infectious risks from inhaling pathogens aerosolized dur...

QUANTITATIVE CANCER RISK ASSESSMENT METHODOLOGY USING SHORT-TERM GENETIC BIOASSAYS: THE COMPARATIVE POTENCY METHOD

EPA Science Inventory

Quantitative risk assessment is fraught with many uncertainties. The validity of the assumptions underlying the methods employed are often difficult to test or validate. Cancer risk assessment has generally employed either human epidemiological data from relatively high occupatio...

Health risk assessment and the practice of industrial hygiene.

PubMed

Paustenbach, D J

1990-07-01

It has been claimed that there may be as many as 2000 airborne chemicals to which persons could be exposed in the workplace and in the community. Of these, occupational exposure limits have been set for approximately 700 chemicals, and only about 30 chemicals have limits for the ambient air. It is likely that some type of health risk assessment methodology will be used to establish limits for the remainder. Although these methods have been used for over 10 yr to set environmental limits, each step of the process (hazard identification, dose-response assessment, exposure assessment, and risk characterization) contains a number of traps into which scientists and risk managers can fall. For example, regulatory approaches to the hazard identification step have allowed little discrimination between the various animal carcinogens, even though these chemicals can vary greatly in their potency and mechanisms of action. In general, epidemiology data have been given little weight compared to the results of rodent bioassays. The dose-response extrapolation process, as generally practiced, often does not present the range of equally plausible values. Procedures which acknowledge and quantitatively account for some or all of the different classes of chemical carcinogens have not been widely adopted. For example, physiologically based pharmacokinetic (PB-PK) and biologically based models need to become a part of future risk assessments. The exposure evaluation portion of risk assessments can now be significantly more valid because of better dispersion models, validated exposure parameters, and the use of computers to account for complex environmental factors. Using these procedures, industrial hygienists are now able to quantitatively estimate the risks caused not only by the inhalation of chemicals but also those caused by dermal contact and incidental ingestion. The appropriate use of risk assessment methods should allow scientists and risk managers to set scientifically valid environmental and occupational standards for air contaminants.

History of EPI Suite™ and future perspectives on chemical property estimation in US Toxic Substances Control Act new chemical risk assessments.

PubMed

Card, Marcella L; Gomez-Alvarez, Vicente; Lee, Wen-Hsiung; Lynch, David G; Orentas, Nerija S; Lee, Mari Titcombe; Wong, Edmund M; Boethling, Robert S

2017-03-22

Chemical property estimation is a key component in many industrial, academic, and regulatory activities, including in the risk assessment associated with the approximately 1000 new chemical pre-manufacture notices the United States Environmental Protection Agency (US EPA) receives annually. The US EPA evaluates fate, exposure and toxicity under the 1976 Toxic Substances Control Act (amended by the 2016 Frank R. Lautenberg Chemical Safety for the 21 st Century Act), which does not require test data with new chemical applications. Though the submission of data is not required, the US EPA has, over the past 40 years, occasionally received chemical-specific data with pre-manufacture notices. The US EPA has been actively using this and publicly available data to develop and refine predictive computerized models, most of which are housed in EPI Suite™, to estimate chemical properties used in the risk assessment of new chemicals. The US EPA develops and uses models based on (quantitative) structure-activity relationships ([Q]SARs) to estimate critical parameters. As in any evolving field, (Q)SARs have experienced successes, suffered failures, and responded to emerging trends. Correlations of a chemical structure with its properties or biological activity were first demonstrated in the late 19 th century and today have been encapsulated in a myriad of quantitative and qualitative SARs. The development and proliferation of the personal computer in the late 20 th century gave rise to a quickly increasing number of property estimation models, and continually improved computing power and connectivity among researchers via the internet are enabling the development of increasingly complex models.

Bovine meat versus pork in Toxoplasma gondii transmission in Italy: A quantitative risk assessment model.

PubMed

Belluco, Simone; Patuzzi, Ilaria; Ricci, Antonia

2018-03-23

Toxoplasma gondii is a widespread zoonotic parasite with a high seroprevalence in the human population and the ability to infect almost all warm blooded animals. Humans can acquire toxoplasmosis from different transmission routes and food plays a critical role. Within the food category, meat is of utmost importance, as it may contain bradyzoites inside tissue cysts, which can potentially cause infection after ingestion if parasites are not inactivated through freezing or cooking before consumption. In Italy, the most commonly consumed meat-producing animal species are bovines and pigs. However, T. gondii prevalence and consumption habits for meat of these animal species are very different. There is debate within the scientific community concerning which of these animal species is the main source of meat-derived human toxoplasmosis. The aim of this work was to build a quantitative risk assessment model to estimate the yearly probability of acquiring toxoplasmosis infection due to consumption of bovine meat and pork (excluding cured products) in Italy, taking into account the different eating habits. The model was fitted with data obtained from the literature regarding: bradyzoite concentrations, portion size, dose-response relation, prevalence of T. gondii in bovines and swine, meat consumption and meat preparation habits. Alternative handling scenarios were considered. The model estimated the risk per year of acquiring T. gondii infection in Italy from bovine and swine meat to be 0.034% and 0.019%, respectively. Results suggest that, due to existing eating habits, bovine meat can be a not negligible source of toxoplasmosis in Italy. Copyright © 2017. Published by Elsevier B.V.

75 FR 4067 - Release of Draft Documents Related to the Review of the National Ambient Air Quality Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-26

... for public comment a second draft assessment document titled, Quantitative Health Risk Assessment for... quantitative analyses that are being conducted as part of the review of the national ambient air quality...-Focused Visibility Assessment--Second External Review Draft and Quantitative Health Risk Assessment for...

75 FR 39252 - Release of Final Documents Related to the Review of the National Ambient Air Quality Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-08

..., Quantitative Health Risk Assessment for Particulate Matter and Particulate Matter Urban-Focused Visibility Assessment. These two documents describe the quantitative analyses that have been conducted as part of the..., Quantitative Health Risk Assessment for Particulate Matter, please contact Dr. Zachary Pekar, Office of Air...

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.

Modeling environmental contamination in hospital single- and four-bed rooms.

PubMed

King, M-F; Noakes, C J; Sleigh, P A

2015-12-01

Aerial dispersion of pathogens is recognized as a potential transmission route for hospital acquired infections; however, little is known about the link between healthcare worker (HCW) contacts' with contaminated surfaces, the transmission of infections and hospital room design. We combine computational fluid dynamics (CFD) simulations of bioaerosol deposition with a validated probabilistic HCW-surface contact model to estimate the relative quantity of pathogens accrued on hands during six types of care procedures in two room types. Results demonstrate that care type is most influential (P < 0.001), followed by the number of surface contacts (P < 0.001) and the distribution of surface pathogens (P = 0.05). Highest hand contamination was predicted during Personal care despite the highest levels of hand hygiene. Ventilation rates of 6 ac/h vs. 4 ac/h showed only minor reductions in predicted hand colonization. Pathogens accrued on hands decreased monotonically after patient care in single rooms due to the physical barrier of bioaerosol transmission between rooms and subsequent hand sanitation. Conversely, contamination was predicted to increase during contact with patients in four-bed rooms due to spatial spread of pathogens. Location of the infectious patient with respect to ventilation played a key role in determining pathogen loadings (P = 0.05). We present the first quantitative model predicting the surface contacts by HCW and the subsequent accretion of pathogenic material as they perform standard patient care. This model indicates that single rooms may significantly reduce the risk of cross-contamination due to indirect infection transmission. Not all care types pose the same risks to patients, and housekeeping performed by HCWs may be an important contribution in the transmission of pathogens between patients. Ventilation rates and positioning of infectious patients within four-bed rooms can mitigate the accretion of pathogens, whereby reducing the risk of missed hand hygiene opportunities. The model provides a tool to quantitatively evaluate the influence of hospital room design on infection risk. © 2015 The Authors. Indoor Air Published by John Wiley & Sons Ltd.

A Quantitative Microbiological Risk Assessment for Salmonella in Pigs for the European Union.

PubMed

Snary, Emma L; Swart, Arno N; Simons, Robin R L; Domingues, Ana Rita Calado; Vigre, Hakan; Evers, Eric G; Hald, Tine; Hill, Andrew A

2016-03-01

A farm-to-consumption quantitative microbiological risk assessment (QMRA) for Salmonella in pigs in the European Union has been developed for the European Food Safety Authority. The primary aim of the QMRA was to assess the impact of hypothetical reductions of slaughter-pig prevalence and the impact of control measures on the risk of human Salmonella infection. A key consideration during the QMRA development was the characterization of variability between E.U. Member States (MSs), and therefore a generic MS model was developed that accounts for differences in pig production, slaughterhouse practices, and consumption patterns. To demonstrate the parameterization of the model, four case study MSs were selected that illustrate the variability in production of pork meat and products across MSs. For the case study MSs the average probability of illness was estimated to be between 1 in 100,000 and 1 in 10 million servings given consumption of one of the three product types considered (pork cuts, minced meat, and fermented ready-to-eat sausages). Further analyses of the farm-to-consumption QMRA suggest that the vast majority of human risk derives from infected pigs with a high concentration of Salmonella in their feces (≥10(4) CFU/g). Therefore, it is concluded that interventions should be focused on either decreasing the level of Salmonella in the feces of infected pigs, the introduction of a control step at the abattoir to reduce the transfer of feces to the exterior of the pig, or a control step to reduce the level of Salmonella on the carcass post-evisceration. © 2016 Society for Risk Analysis.

Exploring the interaction among EPHX1, GSTP1, SERPINE2, and TGFB1 contributing to the quantitative traits of chronic obstructive pulmonary disease in Chinese Han population.

PubMed

An, Li; Lin, Yingxiang; Yang, Ting; Hua, Lin

2016-05-18

Currently, the majority of genetic association studies on chronic obstructive pulmonary disease (COPD) risk focused on identifying the individual effects of single nucleotide polymorphisms (SNPs) as well as their interaction effects on the disease. However, conventional genetic studies often use binary disease status as the primary phenotype, but for COPD, many quantitative traits have the potential correlation with the disease status and closely reflect pathological changes. Here, we genotyped 44 SNPs from four genes (EPHX1, GSTP1, SERPINE2, and TGFB1) in 310 patients and 203 controls which belonged to the Chinese Han population to test the two-way and three-way genetic interactions with COPD-related quantitative traits using recently developed generalized multifactor dimensionality reduction (GMDR) and quantitative multifactor dimensionality reduction (QMDR) algorithms. Based on the 310 patients and the whole samples of 513 subjects, the best gene-gene interactions models were detected for four lung-function-related quantitative traits. For the forced expiratory volume in 1 s (FEV1), the best interaction was seen from EPHX1, SERPINE2, and GSTP1. For FEV1%pre, the forced vital capacity (FVC), and FEV1/FVC, the best interactions were seen from SERPINE2 and TGFB1. The results of this study provide further evidence for the genotype combinations at risk of developing COPD in Chinese Han population and improve the understanding on the genetic etiology of COPD and COPD-related quantitative traits.

Weighted Fuzzy Risk Priority Number Evaluation of Turbine and Compressor Blades Considering Failure Mode Correlations

NASA Astrophysics Data System (ADS)

Gan, Luping; Li, Yan-Feng; Zhu, Shun-Peng; Yang, Yuan-Jian; Huang, Hong-Zhong

2014-06-01

Failure mode, effects and criticality analysis (FMECA) and Fault tree analysis (FTA) are powerful tools to evaluate reliability of systems. Although single failure mode issue can be efficiently addressed by traditional FMECA, multiple failure modes and component correlations in complex systems cannot be effectively evaluated. In addition, correlated variables and parameters are often assumed to be precisely known in quantitative analysis. In fact, due to the lack of information, epistemic uncertainty commonly exists in engineering design. To solve these problems, the advantages of FMECA, FTA, fuzzy theory, and Copula theory are integrated into a unified hybrid method called fuzzy probability weighted geometric mean (FPWGM) risk priority number (RPN) method. The epistemic uncertainty of risk variables and parameters are characterized by fuzzy number to obtain fuzzy weighted geometric mean (FWGM) RPN for single failure mode. Multiple failure modes are connected using minimum cut sets (MCS), and Boolean logic is used to combine fuzzy risk priority number (FRPN) of each MCS. Moreover, Copula theory is applied to analyze the correlation of multiple failure modes in order to derive the failure probabilities of each MCS. Compared to the case where dependency among multiple failure modes is not considered, the Copula modeling approach eliminates the error of reliability analysis. Furthermore, for purpose of quantitative analysis, probabilities importance weight from failure probabilities are assigned to FWGM RPN to reassess the risk priority, which generalize the definition of probability weight and FRPN, resulting in a more accurate estimation than that of the traditional models. Finally, a basic fatigue analysis case drawn from turbine and compressor blades in aeroengine is used to demonstrate the effectiveness and robustness of the presented method. The result provides some important insights on fatigue reliability analysis and risk priority assessment of structural system under failure correlations.

Quantitative health impact of indoor radon in France.

PubMed

Ajrouche, Roula; Roudier, Candice; Cléro, Enora; Ielsch, Géraldine; Gay, Didier; Guillevic, Jérôme; Marant Micallef, Claire; Vacquier, Blandine; Le Tertre, Alain; Laurier, Dominique

2018-05-08

Radon is the second leading cause of lung cancer after smoking. Since the previous quantitative risk assessment of indoor radon conducted in France, input data have changed such as, estimates of indoor radon concentrations, lung cancer rates and the prevalence of tobacco consumption. The aim of this work was to update the risk assessment of lung cancer mortality attributable to indoor radon in France using recent risk models and data, improving the consideration of smoking, and providing results at a fine geographical scale. The data used were population data (2012), vital statistics on death from lung cancer (2008-2012), domestic radon exposure from a recent database that combines measurement results of indoor radon concentration and the geogenic radon potential map for France (2015), and smoking prevalence (2010). The risk model used was derived from a European epidemiological study, considering that lung cancer risk increased by 16% per 100 becquerels per cubic meter (Bq/m 3 ) indoor radon concentration. The estimated number of lung cancer deaths attributable to indoor radon exposure is about 3000 (1000; 5000), which corresponds to about 10% of all lung cancer deaths each year in France. About 33% of lung cancer deaths attributable to radon are due to exposure levels above 100 Bq/m 3 . Considering the combined effect of tobacco and radon, the study shows that 75% of estimated radon-attributable lung cancer deaths occur among current smokers, 20% among ex-smokers and 5% among never-smokers. It is concluded that the results of this study, which are based on precise estimates of indoor radon concentrations at finest geographical scale, can serve as a basis for defining French policy against radon risk.

Development of innovative methods for risk assessment in high-rise construction based on clustering of risk factors

NASA Astrophysics Data System (ADS)

Okolelova, Ella; Shibaeva, Marina; Shalnev, Oleg

2018-03-01

The article analyses risks in high-rise construction in terms of investment value with account of the maximum probable loss in case of risk event. The authors scrutinized the risks of high-rise construction in regions with various geographic, climatic and socio-economic conditions that may influence the project environment. Risk classification is presented in general terms, that includes aggregated characteristics of risks being common for many regions. Cluster analysis tools, that allow considering generalized groups of risk depending on their qualitative and quantitative features, were used in order to model the influence of the risk factors on the implementation of investment project. For convenience of further calculations, each type of risk is assigned a separate code with the number of the cluster and the subtype of risk. This approach and the coding of risk factors makes it possible to build a risk matrix, which greatly facilitates the task of determining the degree of impact of risks. The authors clarified and expanded the concept of the price risk, which is defined as the expected value of the event, 105 which extends the capabilities of the model, allows estimating an interval of the probability of occurrence and also using other probabilistic methods of calculation.

Adoption of Building Information Modelling in project planning risk management

NASA Astrophysics Data System (ADS)

Mering, M. M.; Aminudin, E.; Chai, C. S.; Zakaria, R.; Tan, C. S.; Lee, Y. Y.; Redzuan, A. A.

2017-11-01

An efficient and effective risk management required a systematic and proper methodology besides knowledge and experience. However, if the risk management is not discussed from the starting of the project, this duty is notably complicated and no longer efficient. This paper presents the adoption of Building Information Modelling (BIM) in project planning risk management. The objectives is to identify the traditional risk management practices and its function, besides, determine the best function of BIM in risk management and investigating the efficiency of adopting BIM-based risk management during the project planning phase. In order to obtain data, a quantitative approach is adopted in this research. Based on data analysis, the lack of compliance with project requirements and failure to recognise risk and develop responses to opportunity are the risks occurred when traditional risk management is implemented. When using BIM in project planning, it works as the tracking of cost control and cash flow give impact on the project cycle to be completed on time. 5D cost estimation or cash flow modeling benefit risk management in planning, controlling and managing budget and cost reasonably. There were two factors that mostly benefit a BIM-based technology which were formwork plan with integrated fall plan and design for safety model check. By adopting risk management, potential risks linked with a project and acknowledging to those risks can be identified to reduce them to an acceptable extent. This means recognizing potential risks and avoiding threat by reducing their negative effects. The BIM-based risk management can enhance the planning process of construction projects. It benefits the construction players in various aspects. It is important to know the application of BIM-based risk management as it can be a lesson learnt to others to implement BIM and increase the quality of the project.

Integrated presentation of ecological risk from multiple stressors

PubMed Central

Goussen, Benoit; Price, Oliver R.; Rendal, Cecilie; Ashauer, Roman

2016-01-01

Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic. PMID:27782171

Two approaches to improving mental health care: positivist/quantitative versus skill-based/qualitative.

PubMed

Luchins, Daniel

2012-01-01

The quality improvement model currently used in medicine and mental health was adopted from industry, where it developed out of early 20th-century efforts to apply a positivist/quantitative agenda to improving manufacturing. This article questions the application of this model to mental health care. It argues that (1) developing "operational definitions" for something as value-laden as "quality" risks conflating two realms, what we measure with what we value; (2) when measurements that are tied to individuals are aggregated to establish benchmarks and goals, unwarranted mathematical assumptions are made; (3) choosing clinical outcomes is problematic; (4) there is little relationship between process measures and clinical outcomes; and (5) since changes in quality indices do not relate to improved clinical care, management's reliance on such indices provides an illusory sense of control. An alternative model is the older, skill-based/qualitative approach to knowing, which relies on "implicit/ expert" knowledge. These two approaches offer a series of contrasts: quality versus excellence, competence versus expertise, management versus leadership, extrinsic versus intrinsic rewards. The article concludes that we need not totally dispense with the current quality improvement model, but rather should balance quantitative efforts with the older qualitative approach in a mixed methods model.

Reduced order models for prediction of groundwater quality impacts from CO₂ and brine leakage

DOE PAGES

Zheng, Liange; Carroll, Susan; Bianchi, Marco; ...

2014-12-31

A careful assessment of the risk associated with geologic CO₂ storage is critical to the deployment of large-scale storage projects. A potential risk is the deterioration of groundwater quality caused by the leakage of CO₂ and brine leakage from deep subsurface reservoirs. In probabilistic risk assessment studies, numerical modeling is the primary tool employed to assess risk. However, the application of traditional numerical models to fully evaluate the impact of CO₂ leakage on groundwater can be computationally complex, demanding large processing times and resources, and involving large uncertainties. As an alternative, reduced order models (ROMs) can be used as highlymore » efficient surrogates for the complex process-based numerical models. In this study, we represent the complex hydrogeological and geochemical conditions in a heterogeneous aquifer and subsequent risk by developing and using two separate ROMs. The first ROM is derived from a model that accounts for the heterogeneous flow and transport conditions in the presence of complex leakage functions for CO₂ and brine. The second ROM is obtained from models that feature similar, but simplified flow and transport conditions, and allow for a more complex representation of all relevant geochemical reactions. To quantify possible impacts to groundwater aquifers, the basic risk metric is taken as the aquifer volume in which the water quality of the aquifer may be affected by an underlying CO₂ storage project. The integration of the two ROMs provides an estimate of the impacted aquifer volume taking into account uncertainties in flow, transport and chemical conditions. These two ROMs can be linked in a comprehensive system level model for quantitative risk assessment of the deep storage reservoir, wellbore leakage, and shallow aquifer impacts to assess the collective risk of CO₂ storage projects.« less

Risk measures for power failures in transmission systems

NASA Astrophysics Data System (ADS)

Cassidy, Alex; Feinstein, Zachary; Nehorai, Arye

2016-11-01

We present a novel framework for evaluating the risk of failures in power transmission systems. We use the concept of systemic risk measures from the financial mathematics literature with models of power system failures in order to quantify the risk of the entire power system for design and comparative purposes. The proposed risk measures provide the collection of capacity vectors for the components in the system that lead to acceptable outcomes. Keys to the formulation of our measures of risk are two elements: a model of system behavior that provides the (distribution of) outcomes based on component capacities and an acceptability criterion that determines whether a (random) outcome is acceptable from an aggregated point of view. We examine the effects of altering the line capacities on energy not served under a variety of networks, flow manipulation methods, load shedding schemes, and load profiles using Monte Carlo simulations. Our results provide a quantitative comparison of the performance of these schemes, measured by the required line capacity. These results provide more complete descriptions of the risks of power failures than the previous, one-dimensional metrics.

Methodology for Developing a Probabilistic Risk Assessment Model of Spacecraft Rendezvous and Dockings

NASA Technical Reports Server (NTRS)

Farnham, Steven J., II; Garza, Joel, Jr.; Castillo, Theresa M.; Lutomski, Michael

2011-01-01

In 2007 NASA was preparing to send two new visiting vehicles carrying logistics and propellant to the International Space Station (ISS). These new vehicles were the European Space Agency s (ESA) Automated Transfer Vehicle (ATV), the Jules Verne, and the Japanese Aerospace and Explorations Agency s (JAXA) H-II Transfer Vehicle (HTV). The ISS Program wanted to quantify the increased risk to the ISS from these visiting vehicles. At the time, only the Shuttle, the Soyuz, and the Progress vehicles rendezvoused and docked to the ISS. The increased risk to the ISS was from an increase in vehicle traffic, thereby, increasing the potential catastrophic collision during the rendezvous and the docking or berthing of the spacecraft to the ISS. A universal method of evaluating the risk of rendezvous and docking or berthing was created by the ISS s Risk Team to accommodate the increasing number of rendezvous and docking or berthing operations due to the increasing number of different spacecraft, as well as the future arrival of commercial spacecraft. Before the first docking attempt of ESA's ATV and JAXA's HTV to the ISS, a probabilistic risk model was developed to quantitatively calculate the risk of collision of each spacecraft with the ISS. The 5 rendezvous and docking risk models (Soyuz, Progress, Shuttle, ATV, and HTV) have been used to build and refine the modeling methodology for rendezvous and docking of spacecrafts. This risk modeling methodology will be NASA s basis for evaluating the addition of future ISS visiting spacecrafts hazards, including SpaceX s Dragon, Orbital Science s Cygnus, and NASA s own Orion spacecraft. This paper will describe the methodology used for developing a visiting vehicle risk model.

Predicting MCI outcome with clinically available MRI and CSF biomarkers

PubMed Central

Heister, D.; Brewer, J.B.; Magda, S.; Blennow, K.

2011-01-01

Objective: To determine the ability of clinically available volumetric MRI (vMRI) and CSF biomarkers, alone or in combination with a quantitative learning measure, to predict conversion to Alzheimer disease (AD) in patients with mild cognitive impairment (MCI). Methods: We stratified 192 MCI participants into positive and negative risk groups on the basis of 1) degree of learning impairment on the Rey Auditory Verbal Learning Test; 2) medial temporal atrophy, quantified from Food and Drug Administration–approved software for automated vMRI analysis; and 3) CSF biomarker levels. We also stratified participants based on combinations of risk factors. We computed Cox proportional hazards models, controlling for age, to assess 3-year risk of converting to AD as a function of risk group and used Kaplan-Meier analyses to determine median survival times. Results: When risk factors were examined separately, individuals testing positive showed significantly higher risk of converting to AD than individuals testing negative (hazard ratios [HR] 1.8–4.1). The joint presence of any 2 risk factors substantially increased risk, with the combination of greater learning impairment and increased atrophy associated with highest risk (HR 29.0): 85% of patients with both risk factors converted to AD within 3 years, vs 5% of those with neither. The presence of medial temporal atrophy was associated with shortest median dementia-free survival (15 months). Conclusions: Incorporating quantitative assessment of learning ability along with vMRI or CSF biomarkers in the clinical workup of MCI can provide critical information on risk of imminent conversion to AD. PMID:21998317

Illness perceptions and explanatory models of viral hepatitis B & C among immigrants and refugees: a narrative systematic review.

PubMed

Owiti, John A; Greenhalgh, Trisha; Sweeney, Lorna; Foster, Graham R; Bhui, Kamaldeep S

2015-02-15

Hepatitis B and C (HBV, HCV) infections are associated with high morbidity and mortality. Many countries with traditionally low prevalence (such as UK) are now planning interventions (screening, vaccination, and treatment) of high-risk immigrants from countries with high prevalence. This review aimed to synthesise the evidence on immigrants' knowledge of HBV and HCV that might influence the uptake of clinical interventions. The review was also used to inform the design and successful delivery of a randomised controlled trial of targeted screening and treatment. Five databases (PubMed, CINHAL, SOCIOFILE, PsycINFO & Web of Science) were systematically searched, supplemented by reference tracking, searches of selected journals, and of relevant websites. We aimed to identify qualitative and quantitative studies that investigated knowledge of HBV and HCV among immigrants from high endemic areas to low endemic areas. Evidence, extracted according to a conceptual framework of Kleinman's explanatory model, was subjected to narrative synthesis. We adapted the PEN-3 model to categorise and analyse themes, and recommend strategies for interventions to influence help-seeking behaviour. We identified 51 publications including quantitative (n = 39), qualitative (n = 11), and mixed methods (n = 1) designs. Most of the quantitative studies included small samples and had heterogeneous methods and outcomes. The studies mainly concentrated on hepatitis B and ethnic groups of South East Asian immigrants residing in USA, Canada, and Australia. Many immigrants lacked adequate knowledge of aetiology, symptoms, transmission risk factors, prevention strategies, and treatment, of hepatitis HBV and HCV. Ethnicity, gender, better education, higher income, and English proficiency influenced variations in levels and forms of knowledge. Immigrants are vulnerable to HBV and HCV, and risk life-threatening complications from these infections because of poor knowledge and help-seeking behaviour. Primary studies in this area are extremely diverse and of variable quality precluding meta-analysis. Further research is needed outside North America and Australia.

A multicriteria decision analysis model and risk assessment framework for carbon capture and storage.

PubMed

Humphries Choptiany, John Michael; Pelot, Ronald

2014-09-01

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life-cycle assessments and cost-benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO2 emissions from fossil-fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO2 storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high-level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions. © 2014 Society for Risk Analysis.

How TK-TD and population models for aquatic macrophytes could support the risk assessment for plant protection products.

PubMed

Hommen, Udo; Schmitt, Walter; Heine, Simon; Brock, Theo Cm; Duquesne, Sabine; Manson, Phil; Meregalli, Giovanna; Ochoa-Acuña, Hugo; van Vliet, Peter; Arts, Gertie

2016-01-01

This case study of the Society of Environmental Toxicology and Chemistry (SETAC) workshop MODELINK demonstrates the potential use of mechanistic effects models for macrophytes to extrapolate from effects of a plant protection product observed in laboratory tests to effects resulting from dynamic exposure on macrophyte populations in edge-of-field water bodies. A standard European Union (EU) risk assessment for an example herbicide based on macrophyte laboratory tests indicated risks for several exposure scenarios. Three of these scenarios are further analyzed using effect models for 2 aquatic macrophytes, the free-floating standard test species Lemna sp., and the sediment-rooted submerged additional standard test species Myriophyllum spicatum. Both models include a toxicokinetic (TK) part, describing uptake and elimination of the toxicant, a toxicodynamic (TD) part, describing the internal concentration-response function for growth inhibition, and a description of biomass growth as a function of environmental factors to allow simulating seasonal dynamics. The TK-TD models are calibrated and tested using laboratory tests, whereas the growth models were assumed to be fit for purpose based on comparisons of predictions with typical growth patterns observed in the field. For the risk assessment, biomass dynamics are predicted for the control situation and for several exposure levels. Based on specific protection goals for macrophytes, preliminary example decision criteria are suggested for evaluating the model outputs. The models refined the risk indicated by lower tier testing for 2 exposure scenarios, while confirming the risk associated for the third. Uncertainties related to the experimental and the modeling approaches and their application in the risk assessment are discussed. Based on this case study and the assumption that the models prove suitable for risk assessment once fully evaluated, we recommend that 1) ecological scenarios be developed that are also linked to the exposure scenarios, and 2) quantitative protection goals be set to facilitate the interpretation of model results for risk assessment. © 2015 SETAC.

Quantifying links between stroke and risk factors: a study on individual health risk appraisal of stroke in a community of Chongqing.

PubMed

Wu, Yazhou; Zhang, Ling; Yuan, Xiaoyan; Wu, Yamin; Yi, Dong

2011-04-01

The objective of this study is to investigate the risk factors of stroke in a community in Chongqing by setting quantitative criteria for determining the risk factors of stroke. Thus, high-risk individuals can be identified and laid a foundation for predicting individual risk of stroke. 1,034 cases with 1:2 matched controls (2,068) were chosen from five communities in Chongqing including Shapingba, Xiaolongkan, Tianxingqiao, Yubei Road and Ciqikou. Participants were interviewed with a uniform questionnaire. The risk factors of stroke and the odds ratios of risk factors were analyzed with a logistic regression model, and risk exposure factors of different levels were converted into risk scores using statistical models. For men, ten risk factors including hypertension (5.728), family history of stroke (4.599), and coronary heart disease (5.404), among others, were entered into the main effect model. For women, 11 risk factors included hypertension (5.270), family history of stroke (4.866), hyperlipidemia (4.346), among others. The related risk scores were added to obtain a combined risk score to predict the individual's risk of stoke in the future. An individual health risk appraisal model of stroke, which was applicable to individuals of different gender, age, health behavior, disease and family history, was established. In conclusion, personal diseases including hypertension, diabetes mellitus, etc., were very important to the prevalence of stoke. The prevalence of stroke can be effectively reduced by changing unhealthy lifestyles and curing the positive individual disease. The study lays a foundation for health education to persuade people to change their unhealthy lifestyles or behaviors, and could be used in community health services.

Evaluation of New Zealand’s High-Seas Bottom Trawl Closures Using Predictive Habitat Models and Quantitative Risk Assessment

PubMed Central

Penney, Andrew J.; Guinotte, John M.

2013-01-01

United Nations General Assembly Resolution 61/105 on sustainable fisheries (UNGA 2007) establishes three difficult questions for participants in high-seas bottom fisheries to answer: 1) Where are vulnerable marine systems (VMEs) likely to occur?; 2) What is the likelihood of fisheries interaction with these VMEs?; and 3) What might qualify as adequate conservation and management measures to prevent significant adverse impacts? This paper develops an approach to answering these questions for bottom trawling activities in the Convention Area of the South Pacific Regional Fisheries Management Organisation (SPRFMO) within a quantitative risk assessment and cost : benefit analysis framework. The predicted distribution of deep-sea corals from habitat suitability models is used to answer the first question. Distribution of historical bottom trawl effort is used to answer the second, with estimates of seabed areas swept by bottom trawlers being used to develop discounting factors for reduced biodiversity in previously fished areas. These are used in a quantitative ecological risk assessment approach to guide spatial protection planning to address the third question. The coral VME likelihood (average, discounted, predicted coral habitat suitability) of existing spatial closures implemented by New Zealand within the SPRFMO area is evaluated. Historical catch is used as a measure of cost to industry in a cost : benefit analysis of alternative spatial closure scenarios. Results indicate that current closures within the New Zealand SPRFMO area bottom trawl footprint are suboptimal for protection of VMEs. Examples of alternative trawl closure scenarios are provided to illustrate how the approach could be used to optimise protection of VMEs under chosen management objectives, balancing protection of VMEs against economic loss to commercial fishers from closure of historically fished areas. PMID:24358162

Evaluation of New Zealand's high-seas bottom trawl closures using predictive habitat models and quantitative risk assessment.

PubMed

Penney, Andrew J; Guinotte, John M

2013-01-01

United Nations General Assembly Resolution 61/105 on sustainable fisheries (UNGA 2007) establishes three difficult questions for participants in high-seas bottom fisheries to answer: 1) Where are vulnerable marine systems (VMEs) likely to occur?; 2) What is the likelihood of fisheries interaction with these VMEs?; and 3) What might qualify as adequate conservation and management measures to prevent significant adverse impacts? This paper develops an approach to answering these questions for bottom trawling activities in the Convention Area of the South Pacific Regional Fisheries Management Organisation (SPRFMO) within a quantitative risk assessment and cost : benefit analysis framework. The predicted distribution of deep-sea corals from habitat suitability models is used to answer the first question. Distribution of historical bottom trawl effort is used to answer the second, with estimates of seabed areas swept by bottom trawlers being used to develop discounting factors for reduced biodiversity in previously fished areas. These are used in a quantitative ecological risk assessment approach to guide spatial protection planning to address the third question. The coral VME likelihood (average, discounted, predicted coral habitat suitability) of existing spatial closures implemented by New Zealand within the SPRFMO area is evaluated. Historical catch is used as a measure of cost to industry in a cost : benefit analysis of alternative spatial closure scenarios. Results indicate that current closures within the New Zealand SPRFMO area bottom trawl footprint are suboptimal for protection of VMEs. Examples of alternative trawl closure scenarios are provided to illustrate how the approach could be used to optimise protection of VMEs under chosen management objectives, balancing protection of VMEs against economic loss to commercial fishers from closure of historically fished areas.

Genetic and environmental determinants of violence risk in psychotic disorders: a multivariate quantitative genetic study of 1.8 million Swedish twins and siblings.

PubMed

Sariaslan, A; Larsson, H; Fazel, S

2016-09-01

Patients diagnosed with psychotic disorders (for example, schizophrenia and bipolar disorder) have elevated risks of committing violent acts, particularly if they are comorbid with substance misuse. Despite recent insights from quantitative and molecular genetic studies demonstrating considerable pleiotropy in the genetic architecture of these phenotypes, there is currently a lack of large-scale studies that have specifically examined the aetiological links between psychotic disorders and violence. Using a sample of all Swedish individuals born between 1958 and 1989 (n=3 332 101), we identified a total of 923 259 twin-sibling pairs. Patients were identified using the National Patient Register using validated algorithms based on International Classification of Diseases (ICD) 8-10. Univariate quantitative genetic models revealed that all phenotypes (schizophrenia, bipolar disorder, substance misuse, and violent crime) were highly heritable (h(2)=53-71%). Multivariate models further revealed that schizophrenia was a stronger predictor of violence (r=0.32; 95% confidence interval: 0.30-0.33) than bipolar disorder (r=0.23; 0.21-0.25), and large proportions (51-67%) of these phenotypic correlations were explained by genetic factors shared between each disorder, substance misuse, and violence. Importantly, we found that genetic influences that were unrelated to substance misuse explained approximately a fifth (21%; 20-22%) of the correlation with violent criminality in bipolar disorder but none of the same correlation in schizophrenia (Pbipolar disorder<0.001; Pschizophrenia=0.55). These findings highlight the problems of not disentangling common and unique sources of covariance across genetically similar phenotypes as the latter sources may include aetiologically important clues. Clinically, these findings underline the importance of assessing risk of different phenotypes together and integrating interventions for psychiatric disorders, substance misuse, and violence.

Quantitative Experimental Determination of Primer-Dimer Formation Risk by Free-Solution Conjugate Electrophoresis

PubMed Central

Desmarais, Samantha M.; Leitner, Thomas; Barron, Annelise E.

2012-01-01

DNA barcodes are short, unique ssDNA primers that “mark” individual biomolecules. To gain better understanding of biophysical parameters constraining primer-dimer formation between primers that incorporate barcode sequences, we have developed a capillary electrophoresis method that utilizes drag-tag-DNA conjugates to quantify dimerization risk between primer-barcode pairs. Results obtained with this unique free-solution conjugate electrophoresis (FSCE) approach are useful as quantitatively precise input data to parameterize computation models of dimerization risk. A set of fluorescently labeled, model primer-barcode conjugates were designed with complementary regions of differing lengths to quantify heterodimerization as a function of temperature. Primer-dimer cases comprised two 30-mer primers, one of which was covalently conjugated to a lab-made, chemically synthesized poly-N-methoxyethylglycine drag-tag, which reduced electrophoretic mobility of ssDNA to distinguish it from ds primer-dimers. The drag-tags also provided a shift in mobility for the dsDNA species, which allowed us to quantitate primer-dimer formation. In the experimental studies, pairs of oligonucleotide primer-barcodes with fully or partially complementary sequences were annealed, and then separated by free-solution conjugate CE at different temperatures, to assess effects on primer-dimer formation. When less than 30 out of 30 basepairs were bonded, dimerization was inversely correlated to temperature. Dimerization occurred when more than 15 consecutive basepairs formed, yet non-consecutive basepairs did not create stable dimers even when 20 out of 30 possible basepairs bonded. The use of free-solution electrophoresis in combination with a peptoid drag-tag and different fluorophores enabled precise separation of short DNA fragments to establish a new mobility shift assay for detection of primer-dimer formation. PMID:22331820

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.

Risk Management in Complex Construction Projects that Apply Renewable Energy Sources: A Case Study of the Realization Phase of the Energis Educational and Research Intelligent Building

NASA Astrophysics Data System (ADS)

Krechowicz, Maria

2017-10-01

Nowadays, one of the characteristic features of construction industry is an increased complexity of a growing number of projects. Almost each construction project is unique, has its project-specific purpose, its own project structural complexity, owner’s expectations, ground conditions unique to a certain location, and its own dynamics. Failure costs and costs resulting from unforeseen problems in complex construction projects are very high. Project complexity drivers pose many vulnerabilities to a successful completion of a number of projects. This paper discusses the process of effective risk management in complex construction projects in which renewable energy sources were used, on the example of the realization phase of the ENERGIS teaching-laboratory building, from the point of view of DORBUD S.A., its general contractor. This paper suggests a new approach to risk management for complex construction projects in which renewable energy sources were applied. The risk management process was divided into six stages: gathering information, identification of the top, critical project risks resulting from the project complexity, construction of the fault tree for each top, critical risks, logical analysis of the fault tree, quantitative risk assessment applying fuzzy logic and development of risk response strategy. A new methodology for the qualitative and quantitative risk assessment for top, critical risks in complex construction projects was developed. Risk assessment was carried out applying Fuzzy Fault Tree analysis on the example of one top critical risk. Application of the Fuzzy sets theory to the proposed model allowed to decrease uncertainty and eliminate problems with gaining the crisp values of the basic events probability, common during expert risk assessment with the objective to give the exact risk score of each unwanted event probability.

A non-Gaussian approach to risk measures

NASA Astrophysics Data System (ADS)

Bormetti, Giacomo; Cisana, Enrica; Montagna, Guido; Nicrosini, Oreste

2007-03-01

Reliable calculations of financial risk require that the fat-tailed nature of prices changes is included in risk measures. To this end, a non-Gaussian approach to financial risk management is presented, modelling the power-law tails of the returns distribution in terms of a Student- t distribution. Non-Gaussian closed-form solutions for value-at-risk and expected shortfall are obtained and standard formulae known in the literature under the normality assumption are recovered as a special case. The implications of the approach for risk management are demonstrated through an empirical analysis of financial time series from the Italian stock market and in comparison with the results of the most widely used procedures of quantitative finance. Particular attention is paid to quantify the size of the errors affecting the market risk measures obtained according to different methodologies, by employing a bootstrap technique.

TOXNET: Toxicology Data Network

MedlinePlus

... 4. Supporting Data for Carcinogenicity Expand II.B. Quantitative Estimate of Carcinogenic Risk from Oral Exposure II. ... of Confidence (Carcinogenicity, Oral Exposure) Expand II.C. Quantitative Estimate of Carcinogenic Risk from Inhalation Exposure II. ...

Quality-by-Design II: Application of Quantitative Risk Analysis to the Formulation of Ciprofloxacin Tablets.

PubMed

Claycamp, H Gregg; Kona, Ravikanth; Fahmy, Raafat; Hoag, Stephen W

2016-04-01

Qualitative risk assessment methods are often used as the first step to determining design space boundaries; however, quantitative assessments of risk with respect to the design space, i.e., calculating the probability of failure for a given severity, are needed to fully characterize design space boundaries. Quantitative risk assessment methods in design and operational spaces are a significant aid to evaluating proposed design space boundaries. The goal of this paper is to demonstrate a relatively simple strategy for design space definition using a simplified Bayesian Monte Carlo simulation. This paper builds on a previous paper that used failure mode and effects analysis (FMEA) qualitative risk assessment and Plackett-Burman design of experiments to identity the critical quality attributes. The results show that the sequential use of qualitative and quantitative risk assessments can focus the design of experiments on a reduced set of critical material and process parameters that determine a robust design space under conditions of limited laboratory experimentation. This approach provides a strategy by which the degree of risk associated with each known parameter can be calculated and allocates resources in a manner that manages risk to an acceptable level.

A probabilistic QMRA of Salmonella in direct agricultural reuse of treated municipal wastewater.

PubMed

Amha, Yamrot M; Kumaraswamy, Rajkumari; Ahmad, Farrukh

2015-01-01

Developing reliable quantitative microbial risk assessment (QMRA) procedures aids in setting recommendations on reuse applications of treated wastewater. In this study, a probabilistic QMRA to determine the risk of Salmonella infections resulting from the consumption of edible crops irrigated with treated wastewater was conducted. Quantitative polymerase chain reaction (qPCR) was used to enumerate Salmonella spp. in post-disinfected samples, where they showed concentrations ranging from 90 to 1,600 cells/100 mL. The results were used to construct probabilistic exposure models for the raw consumption of three vegetables (lettuce, cabbage, and cucumber) irrigated with treated wastewater, and to estimate the disease burden using Monte Carlo analysis. The results showed elevated median disease burden, when compared with acceptable disease burden set by the World Health Organization, which is 10⁻⁶ disability-adjusted life years per person per year. Of the three vegetables considered, lettuce showed the highest risk of infection in all scenarios considered, while cucumber showed the lowest risk. The results of the Salmonella concentration obtained with qPCR were compared with the results of Escherichia coli concentration for samples taken on the same sampling dates.

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2012-07-16

... health assessment program that evaluates quantitative and qualitative risk information on effects that..., National Center for Environmental Assessment, (mail code: 8601P), Office of Research and Development, U.S... quantitative and qualitative risk information on effects that may result from exposure to specific chemical...

Common Cause Failure Modeling in Space Launch Vehicles

NASA Technical Reports Server (NTRS)

Hark, Frank; Ring, Rob; Novack, Steven D.; Britton, Paul

2015-01-01

Common Cause Failures (CCFs) are a known and documented phenomenon that defeats system redundancy. CCFs are a set of dependent type of failures that can be caused for example by system environments, manufacturing, transportation, storage, maintenance, and assembly. Since there are many factors that contribute to CCFs, they can be reduced, but are difficult to eliminate entirely. Furthermore, failure databases sometimes fail to differentiate between independent and dependent CCF. Because common cause failure data is limited in the aerospace industry, the Probabilistic Risk Assessment (PRA) Team at Bastion Technology Inc. is estimating CCF risk using generic data collected by the Nuclear Regulatory Commission (NRC). Consequently, common cause risk estimates based on this database, when applied to other industry applications, are highly uncertain. Therefore, it is important to account for a range of values for independent and CCF risk and to communicate the uncertainty to decision makers. There is an existing methodology for reducing CCF risk during design, which includes a checklist of 40+ factors grouped into eight categories. Using this checklist, an approach to produce a beta factor estimate is being investigated that quantitatively relates these factors. In this example, the checklist will be tailored to space launch vehicles, a quantitative approach will be described, and an example of the method will be presented.

Depression as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies.

PubMed

Gao, Yuan; Huang, Changquan; Zhao, Kexiang; Ma, Louyan; Qiu, Xuan; Zhang, Lei; Xiu, Yun; Chen, Lin; Lu, Wei; Huang, Chunxia; Tang, Yong; Xiao, Qian

2013-05-01

This study examined whether depression was a risk factor for onset of dementia including Alzheimer's disease (AD), vascular dementia (VD) and any dementia, and mild cognitive impairment (MCI) by using a quantitative meta-analysis of longitudinal studies. EMBASE and MEDLINE were searched for articles published up to February 2011. All studies that examined the relationship between depression and the onset of dementia or MCI were included. Pooled relative risk was calculated using fixed-effects models. Twelve studies met our inclusion criteria for this meta-analysis. All subjects were without dementia or MCI at baseline. Four, two, five, and four studies compared the incidence of AD, VD, any dementia, and MCI between subjects with or without depression, respectively. After pooling all the studies, subjects with depression had higher incidence of AD (relative risk (RR):1.66, 95% confidence interval (CI): 1.29-2.14), VD (RR: 1.89, 95% CI: 1.19-3.01), any dementia (RR: 1.55, 95% CI: 1.31-2.83), and MCI (RR: 1.97, 95% CI: 1.53-2.54) than those without depression. The quantitative meta-analysis showed that depression was a major risk factor for incidence of dementia (including AD, VD, and any dementia) and MCI. Copyright © 2012 John Wiley & Sons, Ltd.

Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms

PubMed Central

Ying, Jiali; Zhang, Ting; Tang, Meng

2015-01-01

Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests. PMID:28347085

Quantitative approach for the risk assessment of African swine fever and Classical swine fever introduction into the United States through legal imports of pigs and swine products.

PubMed

Herrera-Ibatá, Diana María; Martínez-López, Beatriz; Quijada, Darla; Burton, Kenneth; Mur, Lina

2017-01-01

The US livestock safety strongly depends on its capacity to prevent the introduction of Transboundary Animal Diseases (TADs). Therefore, accurate and updated information on the location and origin of those potential TADs risks is essential, so preventive measures as market restrictions can be put on place. The objective of the present study was to evaluate the current risk of African swine fever (ASF) and Classical swine fever (CSF) introduction into the US through the legal importations of live pigs and swine products using a quantitative approach that could be later applied to other risks. Four quantitative stochastic risk assessment models were developed to estimate the monthly probabilities of ASF and CSF release into the US, and the exposure of susceptible populations (domestic and feral swine) to these introductions at state level. The results suggest a low annual probability of either ASF or CSF introduction into the US, by any of the analyzed pathways (5.5*10-3). Being the probability of introduction through legal imports of live pigs (1.8*10-3 for ASF, and 2.5*10-3 for CSF) higher than the risk of legally imported swine products (8.90*10-4 for ASF, and 1.56*10-3 for CSF). This could be caused due to the low probability of exposure associated with this type of commodity (products). The risk of feral pigs accessing to swine products discarded in landfills was slightly higher than the potential exposure of domestic pigs through swill feeding. The identification of the months at highest risk, the origin of the higher risk imports, and the location of the US states most vulnerable to those introductions (Iowa, Minnesota and Wisconsin for live swine and California, Florida and Texas for swine products), is valuable information that would help to design prevention, risk-mitigation and early-detection strategies that would help to minimize the catastrophic consequences of potential ASF/CSF introductions into the US.

Quantitative approach for the risk assessment of African swine fever and Classical swine fever introduction into the United States through legal imports of pigs and swine products

PubMed Central

Herrera-Ibatá, Diana María; Martínez-López, Beatriz; Quijada, Darla; Burton, Kenneth

2017-01-01

The US livestock safety strongly depends on its capacity to prevent the introduction of Transboundary Animal Diseases (TADs). Therefore, accurate and updated information on the location and origin of those potential TADs risks is essential, so preventive measures as market restrictions can be put on place. The objective of the present study was to evaluate the current risk of African swine fever (ASF) and Classical swine fever (CSF) introduction into the US through the legal importations of live pigs and swine products using a quantitative approach that could be later applied to other risks. Four quantitative stochastic risk assessment models were developed to estimate the monthly probabilities of ASF and CSF release into the US, and the exposure of susceptible populations (domestic and feral swine) to these introductions at state level. The results suggest a low annual probability of either ASF or CSF introduction into the US, by any of the analyzed pathways (5.5*10−3). Being the probability of introduction through legal imports of live pigs (1.8*10−3 for ASF, and 2.5*10−3 for CSF) higher than the risk of legally imported swine products (8.90*10−4 for ASF, and 1.56*10−3 for CSF). This could be caused due to the low probability of exposure associated with this type of commodity (products). The risk of feral pigs accessing to swine products discarded in landfills was slightly higher than the potential exposure of domestic pigs through swill feeding. The identification of the months at highest risk, the origin of the higher risk imports, and the location of the US states most vulnerable to those introductions (Iowa, Minnesota and Wisconsin for live swine and California, Florida and Texas for swine products), is valuable information that would help to design prevention, risk-mitigation and early-detection strategies that would help to minimize the catastrophic consequences of potential ASF/CSF introductions into the US. PMID:28797058

Approaches to acceptable risk

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

Whipple, C

Several alternative approaches to address the question {open_quotes}How safe is safe enough?{close_quotes} are reviewed and an attempt is made to apply the reasoning behind these approaches to the issue of acceptability of radiation exposures received in space. The approaches to the issue of the acceptability of technological risk described here are primarily analytical, and are drawn from examples in the management of environmental health risks. These include risk-based approaches, in which specific quantitative risk targets determine the acceptability of an activity, and cost-benefit and decision analysis, which generally focus on the estimation and evaluation of risks, benefits and costs, inmore » a framework that balances these factors against each other. These analytical methods tend by their quantitative nature to emphasize the magnitude of risks, costs and alternatives, and to downplay other factors, especially those that are not easily expressed in quantitative terms, that affect acceptance or rejection of risk. Such other factors include the issues of risk perceptions and how and by whom risk decisions are made.« less

Conscious worst case definition for risk assessment, part I: a knowledge mapping approach for defining most critical risk factors in integrative risk management of chemicals and nanomaterials.

PubMed

Sørensen, Peter B; Thomsen, Marianne; Assmuth, Timo; Grieger, Khara D; Baun, Anders

2010-08-15

This paper helps bridge the gap between scientists and other stakeholders in the areas of human and environmental risk management of chemicals and engineered nanomaterials. This connection is needed due to the evolution of stakeholder awareness and scientific progress related to human and environmental health which involves complex methodological demands on risk management. At the same time, the available scientific knowledge is also becoming more scattered across multiple scientific disciplines. Hence, the understanding of potentially risky situations is increasingly multifaceted, which again challenges risk assessors in terms of giving the 'right' relative priority to the multitude of contributing risk factors. A critical issue is therefore to develop procedures that can identify and evaluate worst case risk conditions which may be input to risk level predictions. Therefore, this paper suggests a conceptual modelling procedure that is able to define appropriate worst case conditions in complex risk management. The result of the analysis is an assembly of system models, denoted the Worst Case Definition (WCD) model, to set up and evaluate the conditions of multi-dimensional risk identification and risk quantification. The model can help optimize risk assessment planning by initial screening level analyses and guiding quantitative assessment in relation to knowledge needs for better decision support concerning environmental and human health protection or risk reduction. The WCD model facilitates the evaluation of fundamental uncertainty using knowledge mapping principles and techniques in a way that can improve a complete uncertainty analysis. Ultimately, the WCD is applicable for describing risk contributing factors in relation to many different types of risk management problems since it transparently and effectively handles assumptions and definitions and allows the integration of different forms of knowledge, thereby supporting the inclusion of multifaceted risk components in cumulative risk management. Copyright 2009 Elsevier B.V. All rights reserved.

Approaches to advancing quantitative human health risk assessment of environmental chemicals in the post-genomic era.

PubMed

Chiu, Weihsueh A; Euling, Susan Y; Scott, Cheryl Siegel; Subramaniam, Ravi P

2013-09-15

The contribution of genomics and associated technologies to human health risk assessment for environmental chemicals has focused largely on elucidating mechanisms of toxicity, as discussed in other articles in this issue. However, there is interest in moving beyond hazard characterization to making more direct impacts on quantitative risk assessment (QRA)--i.e., the determination of toxicity values for setting exposure standards and cleanup values. We propose that the evolution of QRA of environmental chemicals in the post-genomic era will involve three, somewhat overlapping phases in which different types of approaches begin to mature. The initial focus (in Phase I) has been and continues to be on "augmentation" of weight of evidence--using genomic and related technologies qualitatively to increase the confidence in and scientific basis of the results of QRA. Efforts aimed towards "integration" of these data with traditional animal-based approaches, in particular quantitative predictors, or surrogates, for the in vivo toxicity data to which they have been anchored are just beginning to be explored now (in Phase II). In parallel, there is a recognized need for "expansion" of the use of established biomarkers of susceptibility or risk of human diseases and disorders for QRA, particularly for addressing the issues of cumulative assessment and population risk. Ultimately (in Phase III), substantial further advances could be realized by the development of novel molecular and pathway-based biomarkers and statistical and in silico models that build on anticipated progress in understanding the pathways of human diseases and disorders. Such efforts would facilitate a gradual "reorientation" of QRA towards approaches that more directly link environmental exposures to human outcomes. Published by Elsevier Inc.

Performance of Two Quantitative PCR Methods for Microbial Source Tracking of Human Sewage and Implications for Microbial Risk Assessment in Recreational Waters

PubMed Central

Staley, Christopher; Gordon, Katrina V.; Schoen, Mary E.

2012-01-01

Before new, rapid quantitative PCR (qPCR) methods for assessment of recreational water quality and microbial source tracking (MST) can be useful in a regulatory context, an understanding of the ability of the method to detect a DNA target (marker) when the contaminant source has been diluted in environmental waters is needed. This study determined the limits of detection and quantification of the human-associated Bacteroides sp. (HF183) and human polyomavirus (HPyV) qPCR methods for sewage diluted in buffer and in five ambient, Florida water types (estuarine, marine, tannic, lake, and river). HF183 was quantifiable in sewage diluted up to 10−6 in 500-ml ambient-water samples, but HPyVs were not quantifiable in dilutions of >10−4. Specificity, which was assessed using fecal composites from dogs, birds, and cattle, was 100% for HPyVs and 81% for HF183. Quantitative microbial risk assessment (QMRA) estimated the possible norovirus levels in sewage and the human health risk at various sewage dilutions. When juxtaposed with the MST marker detection limits, the QMRA analysis revealed that HF183 was detectable when the modeled risk of gastrointestinal (GI) illness was at or below the benchmark of 10 illnesses per 1,000 exposures, but the HPyV method was generally not sensitive enough to detect potential health risks at the 0.01 threshold for frequency of illness. The tradeoff between sensitivity and specificity in the MST methods indicates that HF183 data should be interpreted judiciously, preferably in conjunction with a more host-specific marker, and that better methods of concentrating HPyVs from environmental waters are needed if this method is to be useful in a watershed management or monitoring context. PMID:22885746

Earth reencounter probabilities for aborted space disposal of hazardous nuclear waste

NASA Technical Reports Server (NTRS)

Friedlander, A. L.; Feingold, H.

1977-01-01

A quantitative assessment is made of the long-term risk of earth reencounter and reentry associated with aborted disposal of hazardous material in the space environment. Numerical results are presented for 10 candidate disposal options covering a broad spectrum of disposal destinations and deployment propulsion systems. Based on representative models of system failure, the probability that a single payload will return and collide with earth within a period of 250,000 years is found to lie in the range .0002-.006. Proportionately smaller risk attaches to shorter time intervals. Risk-critical factors related to trajectory geometry and system reliability are identified as possible mechanisms of hazard reduction.

OVERVIEW OF SESSION GOALS WITH EXAMPLES OF THE EVOLUTION OF MULTI-MEDIA MODELING FOR INFORMING REGULATORY DECISIONS AT THE U.S. ENVIRONMENTAL PROTECTION AGENCY

EPA Science Inventory

The presence of high concentrations of mercury in fish tissue worldwide has resulted in increased concern of the exposure risks of fish-eating populations. To develop effective regulations and management practices requires a solid quantitative assessment of the entire mercury ex...

Left atrial appendage segmentation and quantitative assisted diagnosis of atrial fibrillation based on fusion of temporal-spatial information.

PubMed

Jin, Cheng; Feng, Jianjiang; Wang, Lei; Yu, Heng; Liu, Jiang; Lu, Jiwen; Zhou, Jie

2018-05-01

In this paper, we present an approach for left atrial appendage (LAA) multi-phase fast segmentation and quantitative assisted diagnosis of atrial fibrillation (AF) based on 4D-CT data. We take full advantage of the temporal dimension information to segment the living, flailed LAA based on a parametric max-flow method and graph-cut approach to build 3-D model of each phase. To assist the diagnosis of AF, we calculate the volumes of 3-D models, and then generate a "volume-phase" curve to calculate the important dynamic metrics: ejection fraction, filling flux, and emptying flux of the LAA's blood by volume. This approach demonstrates more precise results than the conventional approaches that calculate metrics by area, and allows for the quick analysis of LAA-volume pattern changes of in a cardiac cycle. It may also provide insight into the individual differences in the lesions of the LAA. Furthermore, we apply support vector machines (SVMs) to achieve a quantitative auto-diagnosis of the AF by exploiting seven features from volume change ratios of the LAA, and perform multivariate logistic regression analysis for the risk of LAA thrombosis. The 100 cases utilized in this research were taken from the Philips 256-iCT. The experimental results demonstrate that our approach can construct the 3-D LAA geometries robustly compared to manual annotations, and reasonably infer that the LAA undergoes filling, emptying and re-filling, re-emptying in a cardiac cycle. This research provides a potential for exploring various physiological functions of the LAA and quantitatively estimating the risk of stroke in patients with AF. Copyright © 2018 Elsevier Ltd. All rights reserved.

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 .

A methodology for the extraction of quantitative risk indexes from medical injuries compensation claims.

PubMed

Dalle Carbonare, Simona; Folli, Fulvia; Patrini, Emanuele; Bellazzi, Riccardo

2009-01-01

The prevention of adverse events and medical injuries due to malpractice or suboptimal delivery of health care services is one of the major concerns of citizens and Health Care Organizations. One way to understand adverse events is to analyze the compensation requests for medical injuries that are claimed to hospital or health care services. In this paper we describe the results obtained by applying a probabilistic model, called the actuarial model, to analyze 317 cases of injuries with compensation requests collected from 1999 to the first semester of 2007 by the Azienda Ospedaliera (A.O.) of Lodi, in the Northern part of Italy. The approach, adapted from operational and financial risk management, proved to be useful to understand the risk structure in terms of frequency, severity, expected and unexpected loss related to adverse events.

Understanding the Fundamental Principles Underlying the Survival and Efficient Recovery of Multi-Scale Techno-Social Networks Following a WMD Event (A)

DTIC Science & Technology

2016-07-01

Influenza H1N1 modeling working group meeting, European Center for Disease Control ECDC, Stockholm, 19 October 2010 (A.Vespignani, Panelist). We...dynamics and assessing non -pharmaceutical control interventions. METHODS: We modelled the movements of individuals, including patients not infected with...classification of urban areas according to quantitative risk assessment metrics of secondary E-WMD threats. 2. Optimal mobility control strategies informed by

A Systematic Literature Review and Meta-Regression Analysis on Early-Life Energy Restriction and Cancer Risk in Humans.

PubMed

Elands, Rachel J J; Simons, Colinda C J M; Dongen, Martien van; Schouten, Leo J; Verhage, Bas A J; van den Brandt, Piet A; Weijenberg, Matty P

2016-01-01

In animal models, long-term moderate energy restriction (ER) is reported to decelerate carcinogenesis, whereas the effect of severe ER is inconsistent. The impact of early-life ER on cancer risk has never been reviewed systematically and quantitatively based on observational studies in humans. We conducted a systematic review of observational studies and a meta-(regression) analysis on cohort studies to clarify the association between early-life ER and organ site-specific cancer risk. PubMed and EMBASE (1982 -August 2015) were searched for observational studies. Summary relative risks (RRs) were estimated using a random effects model when available ≥3 studies. Twenty-four studies were included. Eleven publications, emanating from seven prospective cohort studies and some reporting on multiple cancer endpoints, met the inclusion criteria for quantitative analysis. Women exposed to early-life ER (ranging from 220-1660 kcal/day) had a higher breast cancer risk than those not exposed (RRRE all ages = 1.28, 95% CI: 1.05-1.56; RRRE for 10-20 years of age = 1.21, 95% CI: 1.09-1.34). Men exposed to early-life ER (ranging from 220-800kcal/day) had a higher prostate cancer risk than those not exposed (RRRE = 1.16, 95% CI: 1.03-1.30). Summary relative risks were not computed for colorectal cancer, because of heterogeneity, and for stomach-, pancreas-, ovarian-, and respiratory cancer because there were <3 available studies. Longer duration of exposure to ER, after adjustment for severity, was positively associated with overall cancer risk in women (p = 0.02). Ecological studies suggest that less severe ER is generally associated with a reduced risk of cancer. Early-life transient severe ER seems to be associated with increased cancer risk in the breast (particularly ER exposure at adolescent age) and prostate. The duration, rather than severity of exposure to ER, seems to positively influence relative risk estimates. This result should be interpreted with caution due to the limited number of studies and difficulty in disentangling duration, severity, and geographical setting of exposure.

A Systematic Literature Review and Meta-Regression Analysis on Early-Life Energy Restriction and Cancer Risk in Humans

PubMed Central

Elands, Rachel J. J.; Simons, Colinda C. J. M.; van Dongen, Martien; Schouten, Leo J.; Verhage, Bas A. J.; van den Brandt, Piet A.; Weijenberg, Matty P.

2016-01-01

Background In animal models, long-term moderate energy restriction (ER) is reported to decelerate carcinogenesis, whereas the effect of severe ER is inconsistent. The impact of early-life ER on cancer risk has never been reviewed systematically and quantitatively based on observational studies in humans. Objective We conducted a systematic review of observational studies and a meta-(regression) analysis on cohort studies to clarify the association between early-life ER and organ site-specific cancer risk. Methods PubMed and EMBASE (1982 –August 2015) were searched for observational studies. Summary relative risks (RRs) were estimated using a random effects model when available ≥3 studies. Results Twenty-four studies were included. Eleven publications, emanating from seven prospective cohort studies and some reporting on multiple cancer endpoints, met the inclusion criteria for quantitative analysis. Women exposed to early-life ER (ranging from 220–1660 kcal/day) had a higher breast cancer risk than those not exposed (RRRE all ages = 1.28, 95% CI: 1.05–1.56; RRRE for 10–20 years of age = 1.21, 95% CI: 1.09–1.34). Men exposed to early-life ER (ranging from 220–800kcal/day) had a higher prostate cancer risk than those not exposed (RRRE = 1.16, 95% CI: 1.03–1.30). Summary relative risks were not computed for colorectal cancer, because of heterogeneity, and for stomach-, pancreas-, ovarian-, and respiratory cancer because there were <3 available studies. Longer duration of exposure to ER, after adjustment for severity, was positively associated with overall cancer risk in women (p = 0.02). Ecological studies suggest that less severe ER is generally associated with a reduced risk of cancer. Conclusions Early-life transient severe ER seems to be associated with increased cancer risk in the breast (particularly ER exposure at adolescent age) and prostate. The duration, rather than severity of exposure to ER, seems to positively influence relative risk estimates. This result should be interpreted with caution due to the limited number of studies and difficulty in disentangling duration, severity, and geographical setting of exposure. PMID:27643873

Biological Based Risk Assessment for Space Exploration

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.

2011-01-01

Exposures from galactic cosmic rays (GCR) - made up of high-energy protons and high-energy and charge (HZE) nuclei, and solar particle events (SPEs) - comprised largely of low- to medium-energy protons are the primary health concern for astronauts for long-term space missions. Experimental studies have shown that HZE nuclei produce both qualitative and quantitative differences in biological effects compared to terrestrial radiation, making risk assessments for cancer and degenerative risks, such as central nervous system effects and heart disease, highly uncertain. The goal for space radiation protection at NASA is to be able to reduce the uncertainties in risk assessments for Mars exploration to be small enough to ensure acceptable levels of risks are not exceeded and to adequately assess the efficacy of mitigation measures such as shielding or biological countermeasures. We review the recent BEIR VII and UNSCEAR-2006 models of cancer risks and their uncertainties. These models are shown to have an inherent 2-fold uncertainty as defined by ratio of the 95% percent confidence level to the mean projection, even before radiation quality is considered. In order to overcome the uncertainties in these models, new approaches to risk assessment are warranted. We consider new computational biology approaches to modeling cancer risks. A basic program of research that includes stochastic descriptions of the physics and chemistry of radiation tracks and biochemistry of metabolic pathways, to emerging biological understanding of cellular and tissue modifications leading to cancer is described.

Validation of the 2014 European Society of Cardiology Sudden Cardiac Death Risk Prediction Model in Hypertrophic Cardiomyopathy in a Reference Center in South America.

PubMed

Fernández, Adrián; Quiroga, Alejandro; Ochoa, Juan Pablo; Mysuta, Mauricio; Casabé, José Horacio; Biagetti, Marcelo; Guevara, Eduardo; Favaloro, Liliana E; Fava, Agostina M; Galizio, Néstor

2016-07-01

Sudden cardiac death (SCD) is a common cause of death in hypertrophic cardiomyopathy (HC). Our aim was to conduct an external and independent validation in South America of the 2014 European Society of Cardiology (ESC) SCD risk prediction model to identify patients requiring an implantable cardioverter defibrillator. This study included 502 consecutive patients with HC followed from March, 1993 to December, 2014. A combined end point of SCD or appropriate implantable cardioverter defibrillator therapy was assessed. For the quantitative estimation of individual 5-year SCD risk, we used the formula: 1 - 0.998(exp(Prognostic index)). Our database also included the abnormal blood pressure response to exercise as a risk marker. We analyzed the 3 categories of 5-year risk proposed by the ESC: low risk (LR) <4%; intermediate risk (IR) ≥4% to <6%, and high risk (HR) ≥6%. The LR group included 387 patients (77%); the IR group 39 (8%); and the HR group 76 (15%). Fourteen patients (3%) had SCD/appropriate implantable cardioverter defibrillator therapy (LR: 0%; IR: 2 of 39 [5%]; and HR: 12 of 76 [16%]). In a receiver-operating characteristic curve, the new model proved to be an excellent predictor because the area under the curve for the estimated risk is 0.925 (statistical C: 0.925; 95% CI 0.8884 to 0.9539, p <0.0001). In conclusion, the SCD risk prediction model in HC proposed by the 2014 ESC guidelines was validated in our population and represents an improvement compared with previous approaches. A larger multicenter, independent and external validation of the model with long-term follow-up would be advisable. Copyright © 2016 Elsevier Inc. All rights reserved.

Human campylobacteriosis related to the consumption of raw milk sold by vending machines in Italy: Quantitative risk assessment based on official controls over four years.

PubMed

Giacometti, Federica; Bonilauri, Paolo; Amatiste, Simonetta; Arrigoni, Norma; Bianchi, Manila; Losio, Marina Nadia; Bilei, Stefano; Cascone, Giuseppe; Comin, Damiano; Daminelli, Paolo; Decastelli, Lucia; Merialdi, Giuseppe; Mioni, Renzo; Peli, Angelo; Petruzzelli, Annalisa; Tonucci, Franco; Piva, Silvia; Serraino, Andrea

2015-09-01

A quantitative risk assessment (RA) model was developed to describe the risk of campylobacteriosis linked to consumption of raw milk sold in vending machines in Italy. Exposure assessment was based on the official microbiological records of raw milk samples from vending machines monitored by the regional Veterinary Authorities from 2008 to 2011, microbial growth during storage, destruction experiments, consumption frequency of raw milk, serving size, consumption preference and age of consumers. The differential risk considered milk handled under regulation conditions (4°C throughout all phases) and the worst time-temperature field handling conditions detected. Two separate RA models were developed, one for the consumption of boiled milk and the other for the consumption of raw milk, and two different dose-response (D-R) relationships were considered. The RA model predicted no human campylobacteriosis cases per year either in the best (4°C) storage conditions or in the case of thermal abuse in case of boiling raw milk, whereas in case of raw milk consumption the annual estimated campylobacteriosis cases depend on the dose-response relationships used in the model (D-R I or D-R II), the milk time-temperature storage conditions, consumer behaviour and age of consumers, namely young (with two cut-off values of ≤5 or ≤6 years old for the sensitive population) versus adult consumers. The annual estimated cases for young consumers using D-R II for the sensitive population (≤5 years old) ranged between 1013.7/100,000 population and 8110.3/100,000 population and for adult consumers using D-R I between 79.4/100,000 population and 333.1/100,000 population. Quantification of the risks associated with raw milk consumption is necessary from a public health perspective and the proposed RA model represents a useful and flexible tool to perform future RAs based on local consumer habits to support decision-making on safety policies. Further educational programmes for raw milk consumers or potential raw milk consumers are required to encourage consumers to boil milk to reduce the associated risk of illness. Copyright © 2015 Elsevier B.V. All rights reserved.

Burnout in Nurses Working With Youth With Chronic Pain: A Mixed-Methods Analysis.

PubMed

Rodrigues, Nikita P; Cohen, Lindsey L; Swartout, Kevin M; Trotochaud, Karen; Murray, Eileen

2018-05-01

Nursing is a rewarding but also challenging profession. Nurses are at risk for burnout and premature exit from the profession, which is detrimental to them, their patients, and the healthcare system. There are few studies examining the unique correlates of burnout in nurses working with pediatric populations. The current 2-study project used mixed-methods (qualitative and then quantitative) analysis to explore burnout in nurses working in an inpatient unit with youth with chronic pain. Study I participants included all of the 32 nurses who worked in an inpatient pediatric unit, which admits patients with chronic pain. Qualitative analyses of focus groups were used to extract themes. These themes were examined via a quantitative battery completed by 41 nurses from 2 inpatient pediatric units with youth with chronic pain. The themes were burnout, moral distress, negative beliefs about chronic pain, barriers to pain management, fear of losing compassion, coworker support as a coping method, time worked in the unit, professional self-efficacy, and negative views of the hospital environment. Quantitative results supported most of the qualitative findings, and taken together, the findings supported a model of burnout in nurses working with youth with chronic pain. Conclusions We integrated qualitative and quantitative findings to develop a model of nurse burnout. This model provides a framework for evaluating and targeting burnout in nurses working with pediatric patients with chronic pain.

Household-level disparities in cancer risks from vehicular air pollution in Miami

NASA Astrophysics Data System (ADS)

Collins, Timothy W.; Grineski, Sara E.; Chakraborty, Jayajit

2015-09-01

Environmental justice (EJ) research has relied on ecological analyses of socio-demographic data from areal units to determine if particular populations are disproportionately burdened by toxic risks. This article advances quantitative EJ research by (a) examining whether statistical associations found for geographic units translate to relationships at the household level; (b) testing alternative explanations for distributional injustices never before investigated; and (c) applying a novel statistical technique appropriate for geographically-clustered data. Our study makes these advances by using generalized estimating equations to examine distributive environmental inequities in the Miami (Florida) metropolitan area, based on primary household-level survey data and census block-level cancer risk estimates of hazardous air pollutant (HAP) exposure from on-road mobile emission sources. In addition to modeling determinants of on-road HAP cancer risk among all survey participants, two subgroup models are estimated to examine whether determinants of risk differ based on disadvantaged minority (Hispanic and non-Hispanic Black) versus non-Hispanic white racial/ethnic status. Results reveal multiple determinants of risk exposure disparities. In the model including all survey participants, renter-occupancy, Hispanic and non-Hispanic black race/ethnicity, the desire to live close to work/urban services or public transportation, and higher risk perception are associated with greater on-road HAP cancer risk; the desire to live in an amenity-rich environment is associated with less risk. Divergent subgroup model results shed light on the previously unexamined role of racial/ethnic status in shaping determinants of risk exposures. While lower socioeconomic status and higher risk perception predict significantly greater on-road HAP cancer risk among disadvantaged minorities, the desire to live near work/urban services or public transport predict significantly greater risk among non-Hispanic whites. Findings have important implications for EJ research and practice in Miami and elsewhere.

Long- and short-time analysis of heartbeat sequences: correlation with mortality risk in congestive heart failure patients.

PubMed

Allegrini, P; Balocchi, R; Chillemi, S; Grigolini, P; Hamilton, P; Maestri, R; Palatella, L; Raffaelli, G

2003-06-01

We analyze RR heartbeat sequences with a dynamic model that satisfactorily reproduces both the long- and the short-time statistical properties of heart beating. These properties are expressed quantitatively by means of two significant parameters, the scaling delta concerning the asymptotic effects of long-range correlation, and the quantity 1-pi establishing the amount of uncorrelated fluctuations. We find a correlation between the position in the phase space (delta, pi) of patients with congestive heart failure and their mortality risk.

Review of NASA approach to space radiation risk assessments for Mars exploration.

PubMed

Cucinotta, Francis A

2015-02-01

Long duration space missions present unique radiation protection challenges due to the complexity of the space radiation environment, which includes high charge and energy particles and other highly ionizing radiation such as neutrons. Based on a recommendation by the National Council on Radiation Protection and Measurements, a 3% lifetime risk of exposure-induced death for cancer has been used as a basis for risk limitation by the National Aeronautics and Space Administration (NASA) for low-Earth orbit missions. NASA has developed a risk-based approach to radiation exposure limits that accounts for individual factors (age, gender, and smoking history) and assesses the uncertainties in risk estimates. New radiation quality factors with associated probability distribution functions to represent the quality factor's uncertainty have been developed based on track structure models and recent radiobiology data for high charge and energy particles. The current radiation dose limits are reviewed for spaceflight and the various qualitative and quantitative uncertainties that impact the risk of exposure-induced death estimates using the NASA Space Cancer Risk (NSCR) model. NSCR estimates of the number of "safe days" in deep space to be within exposure limits and risk estimates for a Mars exploration mission are described.

Assessment and management of human health risk from toxic metals and polycyclic aromatic hydrocarbons in urban stormwater arising from anthropogenic activities and traffic congestion.

PubMed

Ma, Yukun; Liu, An; Egodawatta, Prasanna; McGree, James; Goonetilleke, Ashantha

2017-02-01

Toxic metals (TMs) and polycyclic aromatic hydrocarbons (PAHs) in urban stormwater pose risk to human health, thereby constraining its reuse potential. Based on the hypothesis that stormwater quality is primarily influenced by anthropogenic activities and traffic congestion, the primary focus of the research study was to analyse the impacts on human health risk from TMs and PAHs in urban stormwater and thereby develop a quantitative risk assessment model. The study found that anthropogenic activities and traffic congestion exert influence on the risk posed by TMs and PAHs in stormwater from commercial and residential areas. Motor vehicle related businesses (FVS) and traffic congestion (TC) were identified as two parameters which need to be included as independent variables to improve the model. Based on the study outcomes, approaches for mitigating the risk associated with TMs and PAHs in urban stormwater are discussed. Additionally, a roadmap is presented for the assessment and management of the risk arising from these pollutants. The study outcomes are expected to contribute to reducing the human health risk associated urban stormwater pollution and thereby enhance its reuse potential. Copyright © 2016 Elsevier B.V. All rights reserved.

Climate change impact on infection risks during bathing downstream of sewage emissions from CSOs or WWTPs.

PubMed

Sterk, Ankie; de Man, Heleen; Schijven, Jack F; de Nijs, Ton; de Roda Husman, Ana Maria

2016-11-15

Climate change is expected to influence infection risks while bathing downstream of sewage emissions from combined sewage overflows (CSOs) or waste water treatment plants (WWTPs) due to changes in pathogen influx, rising temperatures and changing flow rates of the receiving waters. In this study, climate change impacts on the surface water concentrations of Campylobacter, Cryptosporidium and norovirus originating from sewage were modelled. Quantitative microbial risk assessment (QMRA) was used to assess changes in risks of infection. In general, infection risks downstream of WWTPs are higher than downstream CSOs. Even though model outputs show an increase in CSO influxes, in combination with changes in pathogen survival, dilution within the sewage system and bathing behaviour, the effects on the infection risks are limited. However, a decrease in dilution capacity of surface waters could have significant impact on the infection risks of relatively stable pathogens like Cryptosporidium and norovirus. Overall, average risks are found to be higher downstream WWTPs compared to CSOs. Especially with regard to decreased flow rates, adaptation measures on treatment at WWTPs may be more beneficial for human health than decreasing CSO events. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Application of Hellison's Teaching Personal and Social Responsibility Model in physical education to improve self-efficacy for adolescents at risk of dropping-out of school.

PubMed

Escartí, Amparo; Gutiérrez, Melchor; Pascual, Carmina; Marín, Diana

2010-11-01

This study evaluated improvement in self-efficacy and personal and social responsibility among at-risk of dropping-out of school adolescents participating in a program in which Hellison's Teaching Personal and Social Responsibility Model was applied in physical education classes during the course of an academic year. Thirty at-risk adolescents aged 13-14 years old (23 boys, 7 girls) were assigned to an intervention group (12 boys and 3 girls) or a comparison group (11 boys, 4 girls), the latter of which did not participate in the program. Quantitative results showed a significant improvement in the students' self-efficacy for enlisting social resources and in self-efficacy for self-regulated learning. Qualitative results showed an improvement in responsibility behaviors of participants in the intervention group. This suggests that the model could be effective for improving psychological and social development in at-risk adolescents, and that physical education classes may be an appropriate arena for working with these young people.

Emerging Tools to Estimate and to Predict Exposures to ...

EPA Pesticide Factsheets

The timely assessment of the human and ecological risk posed by thousands of existing and emerging commercial chemicals is a critical challenge facing EPA in its mission to protect public health and the environment The US EPA has been conducting research to enhance methods used to estimate and forecast exposures for tens of thousands of chemicals. This research is aimed at both assessing risks and supporting life cycle analysis, by developing new models and tools for high throughput exposure screening and prioritization, as well as databases that support these and other tools, especially regarding consumer products. The models and data address usage, and take advantage of quantitative structural activity relationships (QSARs) for both inherent chemical properties and function (why the chemical is a product ingredient). To make them more useful and widely available, the new tools, data and models are designed to be: • Flexible • Intraoperative • Modular (useful to more than one, stand-alone application) • Open (publicly available software) Presented at the Society for Risk Analysis Forum: Risk Governance for Key Enabling Technologies, Venice, Italy, March 1-3, 2017

In situ remediation-released zero-valent iron nanoparticles impair soil ecosystems health: A C. elegans biomarker-based risk assessment.

PubMed

Yang, Ying-Fei; Cheng, Yi-Hsien; Liao, Chung-Min

2016-11-05

There is considerable concern over the potential ecotoxicity to soil ecosystems posed by zero-valent iron nanoparticles (Fe(0) NPs) released from in situ environmental remediation. However, a lack of quantitative risk assessment has hampered the development of appropriate testing methods used in environmental applications. Here we present a novel, empirical approach to assess Fe(0) NPs-associated soil ecosystems health risk using the nematode Caenorhabditis elegans as a model organism. A Hill-based dose-response model describing the concentration-fertility inhibition relationships was constructed. A Weibull model was used to estimate thresholds as a guideline to protect C. elegans from infertility when exposed to waterborne or foodborne Fe(0) NPs. Finally, the risk metrics, exceedance risk (ER) and risk quotient (RQ) of Fe(0) NPs in various depths and distances from remediation sites can then be predicted. We showed that under 50% risk probability (ER=0.5), upper soil layer had the highest infertility risk (95% confidence interval: 13.18-57.40%). The margins of safety and acceptable criteria for soil ecosystems health for using Fe(0) NPs in field scale applications were also recommended. Results showed that RQs are larger than 1 in all soil layers when setting a stricter threshold of ∼1.02mgL(-1) of Fe(0) NPs. This C. elegans biomarker-based risk model affords new insights into the links between widespread use of Fe(0) NPs and environmental risk assessment and offers potential environmental implications of metal-based NPs for in situ remediation. Copyright © 2016 Elsevier B.V. All rights reserved.

Using a Prediction Model to Manage Cyber Security Threats.

PubMed

Jaganathan, Venkatesh; Cherurveettil, Priyesh; Muthu Sivashanmugam, Premapriya

2015-01-01

Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

Using a Prediction Model to Manage Cyber Security Threats

PubMed Central

Muthu Sivashanmugam, Premapriya

2015-01-01

Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization. PMID:26065024

Application of Probabilistic Modeling to Quantify the Reduction Levels of Hepatocellular Carcinoma Risk Attributable to Chronic Aflatoxins Exposure.

PubMed

Wambui, Joseph M; Karuri, Edward G; Ojiambo, Julia A; Njage, Patrick M K

2017-01-01

Epidemiological studies show a definite connection between areas of high aflatoxin content and a high occurrence of human hepatocellular carcinoma (HCC). Hepatitis B virus in individuals further increases the risk of HCC. The two risk factors are prevalent in rural Kenya and continuously predispose the rural populations to HCC. A quantitative cancer risk assessment therefore quantified the levels at which potential pre- and postharvest interventions reduce the HCC risk attributable to consumption of contaminated maize and groundnuts. The assessment applied a probabilistic model to derive probability distributions of HCC cases and percentage reductions levels of the risk from secondary data. Contaminated maize and groundnuts contributed to 1,847 ± 514 and 158 ± 52 HCC cases per annum, respectively. The total contribution of both foods to the risk was additive as it resulted in 2,000 ± 518 cases per annum. Consumption and contamination levels contributed significantly to the risk whereby lower age groups were most affected. Nonetheless, pre- and postharvest interventions might reduce the risk by 23.0-83.4% and 4.8-95.1%, respectively. Therefore, chronic exposure to aflatoxins increases the HCC risk in rural Kenya, but a significant reduction of the risk can be achieved by applying specific pre- and postharvest interventions.

Influences on decision-making for undergoing plastic surgery: a mental models and quantitative assessment.

PubMed

Darisi, Tanya; Thorne, Sarah; Iacobelli, Carolyn

2005-09-01

Research was conducted to gain insight into potential clients' decisions to undergo plastic surgery, their perception of benefits and risks, their judgment of outcomes, and their selection of a plastic surgeon. Semistructured, open-ended interviews were conducted with 60 people who expressed interest in plastic surgery. Qualitative analysis revealed their "mental models" regarding influences on their decision to undergo plastic surgery and their choice of a surgeon. Interview results were used to design a Web-based survey in which 644 individuals considering plastic surgery responded. The desire for change was the most direct motivator to undergo plastic surgery. Improvements to physical well-being were related to emotional and social benefits. When prompted about risks, participants mentioned physical, emotional, and social risks. Surgeon selection was a critical influence on decisions to undergo plastic surgery. Participants gave considerable weight to personal consultation and believed that finding the "right" plastic surgeon would minimize potential risks. Findings from the Web-based survey were similar to the mental models interviews in terms of benefit ratings but differed in risk ratings and surgeon selection criteria. The mental models interviews revealed that interview participants were thoughtful about their decision to undergo plastic surgery and focused on finding the right plastic surgeon.

Qalibra: a general model for food risk-benefit assessment that quantifies variability and uncertainty.

PubMed

Hart, Andy; Hoekstra, Jeljer; Owen, Helen; Kennedy, Marc; Zeilmaker, Marco J; de Jong, Nynke; Gunnlaugsdottir, Helga

2013-04-01

The EU project BRAFO proposed a framework for risk-benefit assessment of foods, or changes in diet, that present both potential risks and potential benefits to consumers (Hoekstra et al., 2012a). In higher tiers of the BRAFO framework, risks and benefits are integrated quantitatively to estimate net health impact measured in DALYs or QALYs (disability- or quality-adjusted life years). This paper describes a general model that was developed by a second EU project, Qalibra, to assist users in conducting these assessments. Its flexible design makes it applicable to a wide range of dietary questions involving different nutrients, contaminants and health effects. Account can be taken of variation between consumers in their diets and also other characteristics relevant to the estimation of risk and benefit, such as body weight, gender and age. Uncertainty in any input parameter may be quantified probabilistically, using probability distributions, or deterministically by repeating the assessment with alternative assumptions. Uncertainties that are not quantified should be evaluated qualitatively. Outputs produced by the model are illustrated using results from a simple assessment of fish consumption. More detailed case studies on oily fish and phytosterols are presented in companion papers. The model can be accessed as web-based software at www.qalibra.eu. Copyright © 2012. Published by Elsevier Ltd.

Quantitative risk assessment model of canine rabies introduction: application to the risk to the European Union from Morocco.

PubMed

Napp, S; Casas, M; Moset, S; Paramio, J L; Casal, J

2010-11-01

Although rabies incidence in humans in Western Europe is low, the repeated importation of rabid animals from enzootic areas threatens the rabies-free status of terrestrial animals and challenges the public health systems in this area. Most rabid animals imported into the European Union (EU) in recent years came from Morocco. The aim of this study was to develop a probabilistic risk assessment model to estimate the probability of rabies introduction, which was applied to the risk to the EU from dogs coming from Morocco. The mean annual probability of rabies introduction was 0.21 (90% CI 0.02-0.65). The pathways that contributed the most to this probability were: (a) EU citizens who adopted a dog in Morocco (59% of the total probability) and (b) EU citizens who travelled with their dog to Morocco by ferry (34% of the total probability). The model showed a marked seasonality in the risk of rabies with almost 40% of the annual probability occurring during the months of July and August. The application of stricter border controls (assuming 100% compliance) would result in a >270-fold reduction in the likelihood of rabies introduction into the EU from Morocco.

Quantitative Microbial Risk Assessment Tutorial - Primer

EPA Science Inventory

This document provides a Quantitative Microbial Risk Assessment (QMRA) primer that organizes QMRA tutorials. The tutorials describe functionality of a QMRA infrastructure, guide the user through software use and assessment options, provide step-by-step instructions for implementi...

Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis.

PubMed

Luo, Xin; Zang, Xiao; Yang, Lin; Huang, Junzhou; Liang, Faming; Rodriguez-Canales, Jaime; Wistuba, Ignacio I; Gazdar, Adi; Xie, Yang; Xiao, Guanghua

2017-03-01

Pathological examination of histopathological slides is a routine clinical procedure for lung cancer diagnosis and prognosis. Although the classification of lung cancer has been updated to become more specific, only a small subset of the total morphological features are taken into consideration. The vast majority of the detailed morphological features of tumor tissues, particularly tumor cells' surrounding microenvironment, are not fully analyzed. The heterogeneity of tumor cells and close interactions between tumor cells and their microenvironments are closely related to tumor development and progression. The goal of this study is to develop morphological feature-based prediction models for the prognosis of patients with lung cancer. We developed objective and quantitative computational approaches to analyze the morphological features of pathological images for patients with NSCLC. Tissue pathological images were analyzed for 523 patients with adenocarcinoma (ADC) and 511 patients with squamous cell carcinoma (SCC) from The Cancer Genome Atlas lung cancer cohorts. The features extracted from the pathological images were used to develop statistical models that predict patients' survival outcomes in ADC and SCC, respectively. We extracted 943 morphological features from pathological images of hematoxylin and eosin-stained tissue and identified morphological features that are significantly associated with prognosis in ADC and SCC, respectively. Statistical models based on these extracted features stratified NSCLC patients into high-risk and low-risk groups. The models were developed from training sets and validated in independent testing sets: a predicted high-risk group versus a predicted low-risk group (for patients with ADC: hazard ratio = 2.34, 95% confidence interval: 1.12-4.91, p = 0.024; for patients with SCC: hazard ratio = 2.22, 95% confidence interval: 1.15-4.27, p = 0.017) after adjustment for age, sex, smoking status, and pathologic tumor stage. The results suggest that the quantitative morphological features of tumor pathological images predict prognosis in patients with lung cancer. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

A financial network perspective of financial institutions' systemic risk contributions

NASA Astrophysics Data System (ADS)

Huang, Wei-Qiang; Zhuang, Xin-Tian; Yao, Shuang; Uryasev, Stan

2016-08-01

This study considers the effects of the financial institutions' local topology structure in the financial network on their systemic risk contribution using data from the Chinese stock market. We first measure the systemic risk contribution with the Conditional Value-at-Risk (CoVaR) which is estimated by applying dynamic conditional correlation multivariate GARCH model (DCC-MVGARCH). Financial networks are constructed from dynamic conditional correlations (DCC) with graph filtering method of minimum spanning trees (MSTs). Then we investigate dynamics of systemic risk contributions of financial institution. Also we study dynamics of financial institution's local topology structure in the financial network. Finally, we analyze the quantitative relationships between the local topology structure and systemic risk contribution with panel data regression analysis. We find that financial institutions with greater node strength, larger node betweenness centrality, larger node closeness centrality and larger node clustering coefficient tend to be associated with larger systemic risk contributions.

Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory

NASA Astrophysics Data System (ADS)

Wu, Qiang; Zhao, Dekang; Wang, Yang; Shen, Jianjun; Mu, Wenping; Liu, Honglei

2017-11-01

Water inrush from coal-seam floors greatly threatens mining safety in North China and is a complex process controlled by multiple factors. This study presents a mathematical assessment system for coal-floor water-inrush risk based on the variable-weight model (VWM) and unascertained measure theory (UMT). In contrast to the traditional constant-weight model (CWM), which assigns a fixed weight to each factor, the VWM varies with the factor-state value. The UMT employs the confidence principle, which is more effective in ordered partition problems than the maximum membership principle adopted in the former mathematical theory. The method is applied to the Datang Tashan Coal Mine in North China. First, eight main controlling factors are selected to construct the comprehensive evaluation index system. Subsequently, an incentive-penalty variable-weight model is built to calculate the variable weights of each factor. Then, the VWM-UMT model is established using the quantitative risk-grade divide of each factor according to the UMT. On this basis, the risk of coal-floor water inrush in Tashan Mine No. 8 is divided into five grades. For comparison, the CWM is also adopted for the risk assessment, and a differences distribution map is obtained between the two methods. Finally, the verification of water-inrush points indicates that the VWM-UMT model is powerful and more feasible and reasonable. The model has great potential and practical significance in future engineering applications.

Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe

PubMed Central

Guis, Helene; Caminade, Cyril; Calvete, Carlos; Morse, Andrew P.; Tran, Annelise; Baylis, Matthew

2012-01-01

Vector-borne diseases are among those most sensitive to climate because the ecology of vectors and the development rate of pathogens within them are highly dependent on environmental conditions. Bluetongue (BT), a recently emerged arboviral disease of ruminants in Europe, is often cited as an illustration of climate's impact on disease emergence, although no study has yet tested this association. Here, we develop a framework to quantitatively evaluate the effects of climate on BT's emergence in Europe by integrating high-resolution climate observations and model simulations within a mechanistic model of BT transmission risk. We demonstrate that a climate-driven model explains, in both space and time, many aspects of BT's recent emergence and spread, including the 2006 BT outbreak in northwest Europe which occurred in the year of highest projected risk since at least 1960. Furthermore, the model provides mechanistic insight into BT's emergence, suggesting that the drivers of emergence across Europe differ between the South and the North. Driven by simulated future climate from an ensemble of 11 regional climate models, the model projects increase in the future risk of BT emergence across most of Europe with uncertainty in rate but not in trend. The framework described here is adaptable and applicable to other diseases, where the link between climate and disease transmission risk can be quantified, permitting the evaluation of scale and uncertainty in climate change's impact on the future of such diseases. PMID:21697167

Asbestos exposure--quantitative assessment of risk

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

Hughes, J.M.; Weill, H.

Methods for deriving quantitative estimates of asbestos-associated health risks are reviewed and their numerous assumptions and uncertainties described. These methods involve extrapolation of risks observed at past relatively high asbestos concentration levels down to usually much lower concentration levels of interest today--in some cases, orders of magnitude lower. These models are used to calculate estimates of the potential risk to workers manufacturing asbestos products and to students enrolled in schools containing asbestos products. The potential risk to workers exposed for 40 yr to 0.5 fibers per milliliter (f/ml) of mixed asbestos fiber type (a permissible workplace exposure limit under considerationmore » by the Occupational Safety and Health Administration (OSHA) ) are estimated as 82 lifetime excess cancers per 10,000 exposed. The risk to students exposed to an average asbestos concentration of 0.001 f/ml of mixed asbestos fiber types for an average enrollment period of 6 school years is estimated as 5 lifetime excess cancers per one million exposed. If the school exposure is to chrysotile asbestos only, then the estimated risk is 1.5 lifetime excess cancers per million. Risks from other causes are presented for comparison; e.g., annual rates (per million) of 10 deaths from high school football, 14 from bicycling (10-14 yr of age), 5 to 20 for whooping cough vaccination. Decisions concerning asbestos products require participation of all parties involved and should only be made after a scientifically defensible estimate of the associated risk has been obtained. In many cases to date, such decisions have been made without adequate consideration of the level of risk or the cost-effectiveness of attempts to lower the potential risk. 73 references.« less

Systems Engineering Metrics: Organizational Complexity and Product Quality Modeling

NASA Technical Reports Server (NTRS)

Mog, Robert A.

1997-01-01

Innovative organizational complexity and product quality models applicable to performance metrics for NASA-MSFC's Systems Analysis and Integration Laboratory (SAIL) missions and objectives are presented. An intensive research effort focuses on the synergistic combination of stochastic process modeling, nodal and spatial decomposition techniques, organizational and computational complexity, systems science and metrics, chaos, and proprietary statistical tools for accelerated risk assessment. This is followed by the development of a preliminary model, which is uniquely applicable and robust for quantitative purposes. Exercise of the preliminary model using a generic system hierarchy and the AXAF-I architectural hierarchy is provided. The Kendall test for positive dependence provides an initial verification and validation of the model. Finally, the research and development of the innovation is revisited, prior to peer review. This research and development effort results in near-term, measurable SAIL organizational and product quality methodologies, enhanced organizational risk assessment and evolutionary modeling results, and 91 improved statistical quantification of SAIL productivity interests.

The 2006 William Feinberg lecture: shifting the paradigm from stroke to global vascular risk estimation.

PubMed

Sacco, Ralph L

2007-06-01

By the year 2010, it is estimated that 18.1 million people worldwide will die annually because of cardiovascular diseases and stroke. "Global vascular risk" more broadly includes the multiple overlapping disease silos of stroke, myocardial infarction, peripheral arterial disease, and vascular death. Estimation of global vascular risk requires consideration of a variety of variables including demographics, environmental behaviors, and risk factors. Data from multiple studies suggest continuous linear relationships between the physiological vascular risk modulators of blood pressure, lipids, and blood glucose rather than treating these conditions as categorical risk factors. Constellations of risk factors may be more relevant than individual categorical components. Exciting work with novel risk factors may also have predictive value in estimates of global vascular risk. Advances in imaging have led to the measurement of subclinical conditions such as carotid intima-media thickness and subclinical brain conditions such as white matter hyperintensities and silent infarcts. These subclinical measurements may be intermediate stages in the transition from asymptomatic to symptomatic vascular events, appear to be associated with the fundamental vascular risk factors, and represent opportunities to more precisely quantitate disease progression. The expansion of studies in molecular epidemiology and detection of genetic markers underlying vascular risks also promises to extend our precision of global vascular risk estimation. Global vascular risk estimation will require quantitative methods that bundle these multi-dimensional data into more precise estimates of future risk. The power of genetic information coupled with data on demographics, risk-inducing behaviors, vascular risk modulators, biomarkers, and measures of subclinical conditions should provide the most realistic approximation of an individual's future global vascular risk. The ultimate public health benefit, however, will depend on not only identification of global vascular risk but also the realization that we can modify this risk and prove the prediction models wrong.

Risk assessment of vector-borne diseases for public health governance.

PubMed

Sedda, L; Morley, D W; Braks, M A H; De Simone, L; Benz, D; Rogers, D J

2014-12-01

In the context of public health, risk governance (or risk analysis) is a framework for the assessment and subsequent management and/or control of the danger posed by an identified disease threat. Generic frameworks in which to carry out risk assessment have been developed by various agencies. These include monitoring, data collection, statistical analysis and dissemination. Due to the inherent complexity of disease systems, however, the generic approach must be modified for individual, disease-specific risk assessment frameworks. The analysis was based on the review of the current risk assessments of vector-borne diseases adopted by the main Public Health organisations (OIE, WHO, ECDC, FAO, CDC etc…). Literature, legislation and statistical assessment of the risk analysis frameworks. This review outlines the need for the development of a general public health risk assessment method for vector-borne diseases, in order to guarantee that sufficient information is gathered to apply robust models of risk assessment. Stochastic (especially spatial) methods, often in Bayesian frameworks are now gaining prominence in standard risk assessment procedures because of their ability to assess accurately model uncertainties. Risk assessment needs to be addressed quantitatively wherever possible, and submitted with its quality assessment in order to enable successful public health measures to be adopted. In terms of current practice, often a series of different models and analyses are applied to the same problem, with results and outcomes that are difficult to compare because of the unknown model and data uncertainties. Therefore, the risk assessment areas in need of further research are identified in this article. Copyright © 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

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.

Human-Associated Fecal Quantitative Polymerase Chain ReactionMeasurements and Simulated Risk of Gastrointestinal Illness in Recreational Waters Contaminated with Raw Sewage

EPA Science Inventory

We used quantitative microbial risk assessment (QMRA) to estimate the risk of gastrointestinal (GI) illness associated with swimming in recreational waters containing different concentrations of human-associated fecal qPCR markers from raw sewage– HF183 and HumM2. The volume/volu...

Analyzing the impacts of global trade and investment on non-communicable diseases and risk factors: a critical review of methodological approaches used in quantitative analyses.

PubMed

Cowling, Krycia; Thow, Anne Marie; Pollack Porter, Keshia

2018-05-24

A key mechanism through which globalization has impacted health is the liberalization of trade and investment, yet relatively few studies to date have used quantitative methods to investigate the impacts of global trade and investment policies on non-communicable diseases and risk factors. Recent reviews of this literature have found heterogeneity in results and a range of quality across studies, which may be in part attributable to a lack of conceptual clarity and methodological inconsistencies. This study is a critical review of methodological approaches used in the quantitative literature on global trade and investment and diet, tobacco, alcohol, and related health outcomes, with the objective of developing recommendations and providing resources to guide future robust, policy relevant research. A review of reviews, expert review, and reference tracing were employed to identify relevant studies, which were evaluated using a novel quality assessment tool designed for this research. Eight review articles and 34 quantitative studies were identified for inclusion. Important ways to improve this literature were identified and discussed: clearly defining exposures of interest and not conflating trade and investment; exploring mechanisms of broader relationships; increasing the use of individual-level data; ensuring consensus and consistency in key confounding variables; utilizing more sector-specific versus economy-wide trade and investment indicators; testing and adequately adjusting for autocorrelation and endogeneity when using longitudinal data; and presenting results from alternative statistical models and sensitivity analyses. To guide the development of future analyses, recommendations for international data sources for selected trade and investment indicators, as well as key gaps in the literature, are presented. More methodologically rigorous and consistent approaches in future quantitative studies on the impacts of global trade and investment policies on non-communicable diseases and risk factors can help to resolve inconsistencies of existing research and generate useful information to guide policy decisions.

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.

Quantitative assessment of the probability of bluetongue virus transmission by bovine semen and effectiveness of preventive measures.

PubMed

Napp, S; Allepuz, A; García-Bocanegra, I; Alba, A; Vilar, M J; Casal, J

2011-03-15

Given that bluetongue (BT) may potentially be transmitted by semen, that the disease has significantly expanded in recent years, and that millions of doses of cattle semen are annually traded throughout the world, the transmission of bluetongue virus (BTV) by semen could have severe consequences in the cattle industry. The hypothesis that infected bulls could excrete BTV in their semen led to restrictions on international trade of ruminant semen and the establishment of measures to prevent BTV transmission by semen. However, neither the risk of BTV transmission by semen nor the effectiveness of these measures was estimated quantitatively. The objective of the study was to assess, in case of introduction of BTV into a bovine semen collection centre (SCC), both the risk of BTV transmission by bovine semen and the risk reduction achieved by some of the preventive measures, by means of a stochastic risk assessment model. The model was applied to different scenarios, depending on for example the type of diagnostic test and the interval between the controls (testing) of donor bulls, or the rate of BTV spread within the SCC. Enzyme-linked immunosorbant assay (ELISA) controls of donor bulls every 60 days seemed to be an ineffective method for reducing the risk of BTV transmission in contrast to polymerase chain reaction (PCR) tests every 28 days. An increase in the rate of spread within the SCC resulted in a reduced risk of BTV transmission by semen. The storage of semen for 30 days prior to dispatch seemed to be an efficient way of reducing the risk of transmission by semen. The sensitivity analysis identified the probability of BTV shedding in semen as a crucial parameter in the probability of BTV transmission by semen. However, there is a great degree of uncertainty associated with this parameter, with significant differences depending on the BTV serotype. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

Joint association of sleep problems and psychosocial working conditions with registered long-term sickness absence. A Danish cohort study.

PubMed

Madsen, Ida Eh; Larsen, Ann D; Thorsen, Sannie V; Pejtersen, Jan H; Rugulies, Reiner; Sivertsen, Børge

2016-07-01

Sleep problems and adverse psychosocial working conditions are associated with increased risk of long-term sickness absence. Because sleep problems affect role functioning they may also exacerbate any effects of psychosocial working conditions and vice versa. We examined whether sleep problems and psychosocial working conditions interact in their associations with long-term sickness absence. We linked questionnaire data from participants to two surveys of random samples of the Danish working population (N=10 752) with registries on long-term sick leave during five years after questionnaire response. We defined sleep problems by self-reported symptoms and/or register data on hypnotics purchases of hypnotics. Psychosocial working conditions included quantitative and emotional demands, influence, supervisor recognition and social support, leadership quality, and social support from colleagues. Using time-to-event models, we calculated hazard ratios (HR) and differences and examined interaction as departure from multiplicativity and additivity. During 40 165 person-years of follow-up, we identified 2313 episodes of long-terms sickness absence. Sleep problems predicted risk of long-term sickness absence [HR 1.54, 95% confidence interval (95% CI) 1.38-1.73]. This association was statistically significantly stronger among participants with high quantitative demands and weaker among those with high supervisor recognition (P<0.0001). High quantitative demands exacerbated the association of sleep problems with risk of long-term sickness absence whereas high supervisor recognition buffered this association. To prevent long-term sickness absence among employees with sleep problems, workplace modifications focusing on quantitative demands and supervisor recognition may be considered. Workplace interventions for these factors may more effectively prevent sickness absence when targeted at this group. The efficacy and effectiveness of such interventions needs to be established in future studies.

Source-term development for a contaminant plume for use by multimedia risk assessment models

NASA Astrophysics Data System (ADS)

Whelan, Gene; McDonald, John P.; Taira, Randal Y.; Gnanapragasam, Emmanuel K.; Yu, Charley; Lew, Christine S.; Mills, William B.

2000-02-01

Multimedia modelers from the US Environmental Protection Agency (EPA) and US Department of Energy (DOE) are collaborating to conduct a comprehensive and quantitative benchmarking analysis of four intermedia models: MEPAS, MMSOILS, PRESTO, and RESRAD. These models represent typical analytically based tools that are used in human-risk and endangerment assessments at installations containing radioactive and hazardous contaminants. The objective is to demonstrate an approach for developing an adequate source term by simplifying an existing, real-world, 90Sr plume at DOE's Hanford installation in Richland, WA, for use in a multimedia benchmarking exercise between MEPAS, MMSOILS, PRESTO, and RESRAD. Source characteristics and a release mechanism are developed and described; also described is a typical process and procedure that an analyst would follow in developing a source term for using this class of analytical tool in a preliminary assessment.

Benefit-risk analysis : a brief review and proposed quantitative approaches.

PubMed

Holden, William L

2003-01-01

Given the current status of benefit-risk analysis as a largely qualitative method, two techniques for a quantitative synthesis of a drug's benefit and risk are proposed to allow a more objective approach. The recommended methods, relative-value adjusted number-needed-to-treat (RV-NNT) and its extension, minimum clinical efficacy (MCE) analysis, rely upon efficacy or effectiveness data, adverse event data and utility data from patients, describing their preferences for an outcome given potential risks. These methods, using hypothetical data for rheumatoid arthritis drugs, demonstrate that quantitative distinctions can be made between drugs which would better inform clinicians, drug regulators and patients about a drug's benefit-risk profile. If the number of patients needed to treat is less than the relative-value adjusted number-needed-to-harm in an RV-NNT analysis, patients are willing to undergo treatment with the experimental drug to derive a certain benefit knowing that they may be at risk for any of a series of potential adverse events. Similarly, the results of an MCE analysis allow for determining the worth of a new treatment relative to an older one, given not only the potential risks of adverse events and benefits that may be gained, but also by taking into account the risk of disease without any treatment. Quantitative methods of benefit-risk analysis have a place in the evaluative armamentarium of pharmacovigilance, especially those that incorporate patients' perspectives.

Scenario analysis of freight vehicle accident risks in Taiwan.

PubMed

Tsai, Ming-Chih; Su, Chien-Chih

2004-07-01

This study develops a quantitative risk model by utilizing Generalized Linear Interactive Model (GLIM) to analyze the major freight vehicle accidents in Taiwan. Eight scenarios are established by interacting three categorical variables of driver ages, vehicle types and road types, each of which contains two levels. The database that consists of 2043 major accidents occurring between 1994 and 1998 in Taiwan is utilized to fit and calibrate the model parameters. The empirical results indicate that accident rates of freight vehicles in Taiwan were high in the scenarios involving trucks and non-freeway systems, while; accident consequences were severe in the scenarios involving mature drivers or non-freeway systems. Empirical evidences also show that there is no significant relationship between accident rates and accident consequences. This is to stress that safety studies that describe risk merely as accident rates rather than the combination of accident rates and consequences by definition might lead to biased risk perceptions. Finally, the study recommends using number of vehicle as an alternative of traffic exposure in commercial vehicle risk analysis. The merits of this would be that it is simple and thus reliable; meanwhile, the resulted risk that is termed as fatalities per vehicle could provide clear and direct policy implications for insurance practices and safety regulations.

Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases.

PubMed

Le Vu, Stéphane; Ratmann, Oliver; Delpech, Valerie; Brown, Alison E; Gill, O Noel; Tostevin, Anna; Fraser, Christophe; Volz, Erik M

2018-06-01

Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

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

Transmission and control of Salmonella in the pig feed chain: a conceptual model.

PubMed

Binter, Claudia; Straver, Judith Maria; Häggblom, Per; Bruggeman, Geert; Lindqvist, Per-Anders; Zentek, Jürgen; Andersson, Mats Gunnar

2011-03-01

Infected breeder pigs and contaminated feed represent potential sources of Salmonella introduction to fattening pig herds and may thereby cause human infections acquired via consumption of contaminated pork. Modelling approaches such as quantitative microbial risk assessment could improve the design of strategies for control and tracing of Salmonella in the feed chain. However, the construction of such models requires a thorough understanding of the dynamics of the feed chain, including production processes, microbial processes and transport logistics. The present article illustrates a conceptual model of Salmonella in the pig feed chain and explores the possibilities for quantitative modelling including identifying major gaps in data. Information was collected from peer-reviewed scientific journals, official documents and reports and by means of interviews with experts from authorities and the feed industry. Data on prevalence of Salmonella in different parts of the feed chain are difficult to compare as observed prevalence may be biased by variations in sampling procedures as well as limitations of the detection methods. There are almost no data on numbers of Salmonella in commodities of the feed chain, which often makes it difficult to evaluate risks, intervention strategies and sampling plans in a quantitative manner. Tracing the source of Salmonella contamination is hampered by the risk of cross-contamination as well as various mixing and partitioning events along the supply chain, which sometimes makes it impossible to trace the origin of a lot back to a batch or producer. Available information points to contaminated feed materials, animal vectors and persistent contamination of production environments as important sources of Salmonella in feed production. Technological procedures such as hydrothermal or acid treatment can be used to control Salmonella in feed production. However, a large fraction of pig feed is produced without decontamination procedures. Prevention of recontamination and control of moisture throughout the chain are thus critical factors for controlling Salmonella in feed production. To verify successful control it is necessary to have monitoring strategies able to detect low levels of Salmonella heterogeneously distributed in large volumes of feed and feed material in bulk. Experience from monitoring programs and research investigations indicates that sampling of dust and sweepings from control points along the production line is an efficient strategy to gain an indication of Salmonella contamination. Copyright © 2010 Elsevier B.V. All rights reserved.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

PubMed

Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan

2016-01-01

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Implementation of a new prenatal care model to reduce office visits and increase connectivity and continuity of care: protocol for a mixed-methods study.

PubMed

Ridgeway, Jennifer L; LeBlanc, Annie; Branda, Megan; Harms, Roger W; Morris, Megan A; Nesbitt, Kate; Gostout, Bobbie S; Barkey, Lenae M; Sobolewski, Susan M; Brodrick, Ellen; Inselman, Jonathan; Baron, Anne; Sivly, Angela; Baker, Misty; Finnie, Dawn; Chaudhry, Rajeev; Famuyide, Abimbola O

2015-12-02

Most low-risk pregnant women receive the standard model of prenatal care with frequent office visits. Research suggests that a reduced schedule of visits among low-risk women could be implemented without increasing adverse maternal or fetal outcomes, but patient satisfaction with these models varies. We aim to determine the effectiveness and feasibility of a new prenatal care model (OB Nest) that enhances a reduced visit model by adding virtual connections that improve continuity of care and patient-directed access to care. This mixed-methods study uses a hybrid effectiveness-implementation design in a single center randomized controlled trial (RCT). Embedding process evaluation in an experimental design like an RCT allows researchers to answer both "Did it work?" and "How or why did it work (or not work)?" when studying complex interventions, as well as providing knowledge for translation into practice after the study. The RE-AIM framework was used to ensure attention to evaluating program components in terms of sustainable adoption and implementation. Low-risk patients recruited from the Obstetrics Division at Mayo Clinic (Rochester, MN) will be randomized to OB Nest or usual care. OB Nest patients will be assigned to a dedicated nursing team, scheduled for 8 pre-planned office visits with a physician or midwife and 6 telephone or online nurse visits (compared to 12 pre-planned physician or midwife office visits in the usual care group), and provided fetal heart rate and blood pressure home monitoring equipment and information on joining an online care community. Quantitative methods will include patient surveys and medical record abstraction. The primary quantitative outcome is patient-reported satisfaction. Other outcomes include fidelity to items on the American Congress of Obstetricians and Gynecologists standards of care list, health care utilization (e.g. numbers of antenatal office visits), and maternal and fetal outcomes (e.g. gestational age at delivery), as well as validated patient-reported measures of pregnancy-related stress and perceived quality of care. Quantitative analysis will be performed according to the intention to treat principle. Qualitative methods will include interviews and focus groups with providers, staff, and patients, and will explore satisfaction, intervention adoption, and implementation feasibility. We will use methods of qualitative thematic analysis at three stages. Mixed methods analysis will involve the use of qualitative data to lend insight to quantitative findings. This study will make important contributions to the literature on reduced visit models by evaluating a novel prenatal care model with components to increase patient connectedness (even with fewer pre-scheduled office visits), as demonstrated on a range of patient-important outcomes. The use of a hybrid effectiveness-implementation approach, as well as attention to patient and provider perspectives on program components and implementation, may uncover important information that can inform long-term feasibility and potentially speed future translation. Trial registration identifier: NCT02082275 Submitted: March 6, 2014.

A Functional Model for the Integration of Gains and Losses under Risk: Implications for the Measurement of Subjective Value

ERIC Educational Resources Information Center

Viegas, Ricardo G.; Oliveira, Armando M.; Garriga-Trillo, Ana; Grieco, Alba

2012-01-01

In order to be treated quantitatively, subjective gains and losses (utilities/disutilities) must be psychologically measured. If legitimate comparisons are sought between them, measurement must be at least interval level, with a common unit. If comparisons of absolute magnitudes across gains and losses are further sought, as in standard…

Evaluating analytic and risk assessment tools to estimate sediment and nutrients losses from agricultural lands in the southern region of the USA

USDA-ARS?s Scientific Manuscript database

Non-point source pollution from agricultural fields is a critical problem associated with water quality impairment in the USA and a low-oxygen environment in the Gulf of Mexico. The use, development and enhancement of qualitative and quantitative models or tools for assessing agricultural runoff qua...

Continental-scale simulation of burn probabilities, flame lengths, and fire size distribution for the United States

Treesearch

Mark A. Finney; Charles W. McHugh; Isaac Grenfell; Karin L. Riley

2010-01-01

Components of a quantitative risk assessment were produced by simulation of burn probabilities and fire behavior variation for 134 fire planning units (FPUs) across the continental U.S. The system uses fire growth simulation of ignitions modeled from relationships between large fire occurrence and the fire danger index Energy Release Component (ERC). Simulations of 10,...

Femoral head shape differences during development may identify hips at risk of degeneration.

PubMed

Vanden Berg-Foels, Wendy S; Schwager, Steven J; Todhunter, Rory J; Reeves, Anthony P

2011-12-01

Developmental dysplasia of the hip (DDH) is a common cause of elevated contact stress and early onset osteoarthritis (OA). We hypothesized that adaptation to focal loading during postnatal development would result in signature changes to the shape of the femoral head secondary center of ossification (SCO). SCO shape was evaluated in a canine model of DDH at ages 14 and 32 weeks. The evolving 3D morphology of the SCO was captured using serial quantitative computed tomography. A discrete medial representation shape model was fit to each SCO and served as the basis for quantitative thickness and bending measurements. Shape measurements were tested for associations with hip subluxation and degeneration. At 32 weeks, the SCO was thinner (flatter) in the perifoveal region, the site of focal loading; a greater bend to the SCO was present lateral to the site of thinning; SCO thinning and bending were associated with less femoral head coverage and with a higher probability of degeneration. Shape changes were not detected at 14 weeks. Measurement and visualization of SCO shape changes due to altered loading may provide a basis for identifying hips at risk of early onset OA and a tool for surgical planning of hip restructuring.

Defining High-Risk Precursor Signaling to Advance Breast Cancer Risk Assessment and Prevention

DTIC Science & Technology

2017-03-01

KEYWORDS: 3. ACCOMPLISHMENTS: Aim 1: Functional analysis of progenitor and stem cells in high-risk tissues. Major Task 1Functional...and stem cells in high-risk tissues. Major Task 1: Quantitation of LP (Luminal Progenitor) and basal stem cell (MASC) populations A. Quantitation of...LP and basal stem cell (MASC) populations We have continued to add patients to the cohorts between months 12 and 24. (This reporting period

Dynamic safety assessment of natural gas stations using Bayesian network.

PubMed

Zarei, Esmaeil; Azadeh, Ali; Khakzad, Nima; Aliabadi, Mostafa Mirzaei; Mohammadfam, Iraj

2017-01-05

Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport

PubMed Central

Collender, Philip A.; Cooke, Olivia C.; Bryant, Lee D.; Kjeldsen, Thomas R.; Remais, Justin V.

2017-01-01

Flooding is known to facilitate infectious disease transmission, yet quantitative research on microbiological risks associated with floods has been limited. Pathogen fate and transport models provide a framework to examine interactions between landscape characteristics, hydrology, and waterborne disease risks, but have not been widely developed for flood conditions. We critically examine capabilities of current hydrological models to represent unusual flow paths, non-uniform flow depths, and unsteady flow velocities that accompany flooding. We investigate the theoretical linkages between hydrodynamic processes and spatio-temporally variable suspension and deposition of pathogens from soils and sediments; pathogen dispersion in flow; and concentrations of constituents influencing pathogen transport and persistence. Identifying gaps in knowledge and modeling practice, we propose a research agenda to strengthen microbial fate and transport modeling applied to inland floods: 1) development of models incorporating pathogen discharges from flooded sources (e.g., latrines), effects of transported constituents on pathogen persistence, and supply-limited pathogen transport; 2) studies assessing parameter identifiability and comparing model performance under varying degrees of process representation, in a range of settings; 3) development of remotely sensed datasets to support modeling of vulnerable, data-poor regions; and 4) collaboration between modelers and field-based researchers to expand the collection of useful data in situ. PMID:28757789

76 FR 58509 - Release of Risk and Exposure Assessment Planning Document for the Review of the National Ambient...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-21

... ISA) provides support for the development of quantitative assessments of risk and exposure for health... conclusions presented in the First Draft ISA provide support for the development of quantitative assessments...

Relating design and environmental variables to reliability

NASA Astrophysics Data System (ADS)

Kolarik, William J.; Landers, Thomas L.

The combination of space application and nuclear power source demands high reliability hardware. The possibilities of failure, either an inability to provide power or a catastrophic accident, must be minimized. Nuclear power experiences on the ground have led to highly sophisticated probabilistic risk assessment procedures, most of which require quantitative information to adequately assess such risks. In the area of hardware risk analysis, reliability information plays a key role. One of the lessons learned from the Three Mile Island experience is that thorough analyses of critical components are essential. Nuclear grade equipment shows some reliability advantages over commercial. However, no statistically significant difference has been found. A recent study pertaining to spacecraft electronics reliability, examined some 2500 malfunctions on more than 300 aircraft. The study classified the equipment failures into seven general categories. Design deficiencies and lack of environmental protection accounted for about half of all failures. Within each class, limited reliability modeling was performed using a Weibull failure model.

NASA space cancer risk model-2014: Uncertainties due to qualitative differences in biological effects of HZE particles

NASA Astrophysics Data System (ADS)

Cucinotta, Francis

Uncertainties in estimating health risks from exposures to galactic cosmic rays (GCR) — comprised of protons and high-energy and charge (HZE) nuclei are an important limitation to long duration space travel. HZE nuclei produce both qualitative and quantitative differences in biological effects compared to terrestrial radiation leading to large uncertainties in predicting risks to humans. Our NASA Space Cancer Risk Model-2012 (NSCR-2012) for estimating lifetime cancer risks from space radiation included several new features compared to earlier models from the National Council on Radiation Protection and Measurements (NCRP) used at NASA. New features of NSCR-2012 included the introduction of NASA defined radiation quality factors based on track structure concepts, a Bayesian analysis of the dose and dose-rate reduction effectiveness factor (DDREF) and its uncertainty, and the use of a never-smoker population to represent astronauts. However, NSCR-2012 did not include estimates of the role of qualitative differences between HZE particles and low LET radiation. In this report we discuss evidence for non-targeted effects increasing cancer risks at space relevant HZE particle absorbed doses in tissue (<0.2 Gy), and for increased tumor lethality due to the propensity for higher rates of metastatic tumors from high LET radiation suggested by animal experiments. The NSCR-2014 model considers how these qualitative differences modify the overall probability distribution functions (PDF) for cancer mortality risk estimates from space radiation. Predictions of NSCR-2014 for International Space Station missions and Mars exploration will be described, and compared to those of our earlier NSCR-2012 model.

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.

[Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data: a review].

PubMed

Wu, Chang-Guang; Li, Sheng; Ren, Hua-Dong; Yao, Xiao-Hua; Huang, Zi-Jie

2012-06-01

Soil loss prediction models such as universal soil loss equation (USLE) and its revised universal soil loss equation (RUSLE) are the useful tools for risk assessment of soil erosion and planning of soil conservation at regional scale. To make a rational estimation of vegetation cover and management factor, the most important parameters in USLE or RUSLE, is particularly important for the accurate prediction of soil erosion. The traditional estimation based on field survey and measurement is time-consuming, laborious, and costly, and cannot rapidly extract the vegetation cover and management factor at macro-scale. In recent years, the development of remote sensing technology has provided both data and methods for the estimation of vegetation cover and management factor over broad geographic areas. This paper summarized the research findings on the quantitative estimation of vegetation cover and management factor by using remote sensing data, and analyzed the advantages and the disadvantages of various methods, aimed to provide reference for the further research and quantitative estimation of vegetation cover and management factor at large scale.

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

Clinical relevance of rare germline sequence variants in cancer genes: evolution and application of classification models.

PubMed

Spurdle, Amanda B

2010-06-01

Multifactorial models developed for BRCA1/2 variant classification have proved very useful for delineating BRCA1/2 variants associated with very high risk of cancer, or with little clinical significance. Recent linkage of this quantitative assessment of risk to clinical management guidelines has provided a basis to standardize variant reporting, variant classification and management of families with such variants, and can theoretically be applied to any disease gene. As proof of principle, the multifactorial approach already shows great promise for application to the evaluation of mismatch repair gene variants identified in families with suspected Lynch syndrome. However there is need to be cautious of the noted limitations and caveats of the current model, some of which may be exacerbated by differences in ascertainment and biological pathways to disease for different cancer syndromes.

IWGT report on quantitative approaches to genotoxicity risk assessment II. Use of point-of-departure (PoD) metrics in defining acceptable exposure limits and assessing human risk

EPA Science Inventory

This is the second of two reports from the International Workshops on Genotoxicity Testing (IWGT) Working Group on Quantitative Approaches to Genetic Toxicology Risk Assessment (the QWG). The first report summarized the discussions and recommendations of the QWG related to the ne...

Design features of graphs in health risk communication: a systematic review.

PubMed

Ancker, Jessica S; Senathirajah, Yalini; Kukafka, Rita; Starren, Justin B

2006-01-01

This review describes recent experimental and focus group research on graphics as a method of communication about quantitative health risks. Some of the studies discussed in this review assessed effect of graphs on quantitative reasoning, others assessed effects on behavior or behavioral intentions, and still others assessed viewers' likes and dislikes. Graphical features that improve the accuracy of quantitative reasoning appear to differ from the features most likely to alter behavior or intentions. For example, graphs that make part-to-whole relationships available visually may help people attend to the relationship between the numerator (the number of people affected by a hazard) and the denominator (the entire population at risk), whereas graphs that show only the numerator appear to inflate the perceived risk and may induce risk-averse behavior. Viewers often preferred design features such as visual simplicity and familiarity that were not associated with accurate quantitative judgments. Communicators should not assume that all graphics are more intuitive than text; many of the studies found that patients' interpretations of the graphics were dependent upon expertise or instruction. Potentially useful directions for continuing research include interactions with educational level and numeracy and successful ways to communicate uncertainty about risk.

Faecal Pathogen Flows and Their Public Health Risks in Urban Environments: A Proposed Approach to Inform Sanitation Planning

PubMed Central

Mills, Freya; Petterson, Susan; Norman, Guy

2018-01-01

Public health benefits are often a key political driver of urban sanitation investment in developing countries, however, pathogen flows are rarely taken systematically into account in sanitation investment choices. While several tools and approaches on sanitation and health risks have recently been developed, this research identified gaps in their ability to predict faecal pathogen flows, to relate exposure risks to the existing sanitation services, and to compare expected impacts of improvements. This paper outlines a conceptual approach that links faecal waste discharge patterns with potential pathogen exposure pathways to quantitatively compare urban sanitation improvement options. An illustrative application of the approach is presented, using a spreadsheet-based model to compare the relative effect on disability-adjusted life years of six sanitation improvement options for a hypothetical urban situation. The approach includes consideration of the persistence or removal of different pathogen classes in different environments; recognition of multiple interconnected sludge and effluent pathways, and of multiple potential sites for exposure; and use of quantitative microbial risk assessment to support prediction of relative health risks for each option. This research provides a step forward in applying current knowledge to better consider public health, alongside environmental and other objectives, in urban sanitation decision making. Further empirical research in specific locations is now required to refine the approach and address data gaps. PMID:29360775

Efficient GIS-based model-driven method for flood risk management and its application in central China

NASA Astrophysics Data System (ADS)

Liu, Y.; Zhou, J.; Song, L.; Zou, Q.; Guo, J.; Wang, Y.

2014-02-01

In recent years, an important development in flood management has been the focal shift from flood protection towards flood risk management. This change greatly promoted the progress of flood control research in a multidisciplinary way. Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, traditional methods, e.g., tight-coupling integration of one or more quantitative models, are not enough to provide decision support for managers. Within this context, this paper presents a beneficial methodological framework to enhance the effectiveness of decision support systems, through the dynamic adaptation of support regarding the needs of the decision-maker. In addition, we illustrate a loose-coupling technical prototype for integrating heterogeneous elements, such as multi-source data, multidisciplinary models, GIS tools and existing systems. The main innovation is the application of model-driven concepts, which put the system in a state of continuous iterative optimization. We define the new system as a model-driven decision support system (MDSS ). Two characteristics that differentiate the MDSS are as follows: (1) it is made accessible to non-technical specialists; and (2) it has a higher level of adaptability and compatibility. Furthermore, the MDSS was employed to manage the flood risk in the Jingjiang flood diversion area, located in central China near the Yangtze River. Compared with traditional solutions, we believe that this model-driven method is efficient, adaptable and flexible, and thus has bright prospects of application for comprehensive flood risk management.

How rapidly does the excess risk of lung cancer decline following quitting smoking? A quantitative review using the negative exponential model.

PubMed

Fry, John S; Lee, Peter N; Forey, Barbara A; Coombs, Katharine J

2013-10-01

The excess lung cancer risk from smoking declines with time quit, but the shape of the decline has never been precisely modelled, or meta-analyzed. From a database of studies of at least 100 cases, we extracted 106 blocks of RRs (from 85 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls (or at-risk) formed the data for fitting the negative exponential model. We estimated the half-life (H, time in years when the excess risk becomes half that for a continuing smoker) for each block, investigated model fit, and studied heterogeneity in H. We also conducted sensitivity analyses allowing for reverse causation, either ignoring short-term quitters (S1) or considering them smokers (S2). Model fit was poor ignoring reverse causation, but much improved for both sensitivity analyses. Estimates of H were similar for all three analyses. For the best-fitting analysis (S1), H was 9.93 (95% CI 9.31-10.60), but varied by sex (females 7.92, males 10.71), and age (<50years 6.98, 70+years 12.99). Given that reverse causation is taken account of, the model adequately describes the decline in excess risk. However, estimates of H may be biased by factors including misclassification of smoking status. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals

PubMed Central

Crooks, Kevin R.; Burdett, Christopher L.; Theobald, David M.; King, Sarah R. B.; Rondinini, Carlo; Boitani, Luigi

2017-01-01

Although habitat fragmentation is often assumed to be a primary driver of extinction, global patterns of fragmentation and its relationship to extinction risk have not been consistently quantified for any major animal taxon. We developed high-resolution habitat fragmentation models and used phylogenetic comparative methods to quantify the effects of habitat fragmentation on the world’s terrestrial mammals, including 4,018 species across 26 taxonomic Orders. Results demonstrate that species with more fragmentation are at greater risk of extinction, even after accounting for the effects of key macroecological predictors, such as body size and geographic range size. Species with higher fragmentation had smaller ranges and a lower proportion of high-suitability habitat within their range, and most high-suitability habitat occurred outside of protected areas, further elevating extinction risk. Our models provide a quantitative evaluation of extinction risk assessments for species, allow for identification of emerging threats in species not classified as threatened, and provide maps of global hotspots of fragmentation for the world’s terrestrial mammals. Quantification of habitat fragmentation will help guide threat assessment and strategic priorities for global mammal conservation. PMID:28673992

Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals.

PubMed

Crooks, Kevin R; Burdett, Christopher L; Theobald, David M; King, Sarah R B; Di Marco, Moreno; Rondinini, Carlo; Boitani, Luigi

2017-07-18

Although habitat fragmentation is often assumed to be a primary driver of extinction, global patterns of fragmentation and its relationship to extinction risk have not been consistently quantified for any major animal taxon. We developed high-resolution habitat fragmentation models and used phylogenetic comparative methods to quantify the effects of habitat fragmentation on the world's terrestrial mammals, including 4,018 species across 26 taxonomic Orders. Results demonstrate that species with more fragmentation are at greater risk of extinction, even after accounting for the effects of key macroecological predictors, such as body size and geographic range size. Species with higher fragmentation had smaller ranges and a lower proportion of high-suitability habitat within their range, and most high-suitability habitat occurred outside of protected areas, further elevating extinction risk. Our models provide a quantitative evaluation of extinction risk assessments for species, allow for identification of emerging threats in species not classified as threatened, and provide maps of global hotspots of fragmentation for the world's terrestrial mammals. Quantification of habitat fragmentation will help guide threat assessment and strategic priorities for global mammal conservation.

Development of economic consequence methodology for process risk analysis.

PubMed

Zadakbar, Omid; Khan, Faisal; Imtiaz, Syed

2015-04-01

A comprehensive methodology for economic consequence analysis with appropriate models for risk analysis of process systems is proposed. This methodology uses loss functions to relate process deviations in a given scenario to economic losses. It consists of four steps: definition of a scenario, identification of losses, quantification of losses, and integration of losses. In this methodology, the process deviations that contribute to a given accident scenario are identified and mapped to assess potential consequences. Losses are assessed with an appropriate loss function (revised Taguchi, modified inverted normal) for each type of loss. The total loss is quantified by integrating different loss functions. The proposed methodology has been examined on two industrial case studies. Implementation of this new economic consequence methodology in quantitative risk assessment will provide better understanding and quantification of risk. This will improve design, decision making, and risk management strategies. © 2014 Society for Risk Analysis.

Quantitative structure-activity relationship modeling on in vitro endocrine effects and metabolic stability involving 26 selected brominated flame retardants.

PubMed

Harju, Mikael; Hamers, Timo; Kamstra, Jorke H; Sonneveld, Edwin; Boon, Jan P; Tysklind, Mats; Andersson, Patrik L

2007-04-01

In this work, quantitative structure-activity relationships (QSARs) were developed to aid human and environmental risk assessment processes for brominated flame retardants (BFRs). Brominated flame retardants, such as the high-production-volume chemicals polybrominated diphenyl ethers (PBDEs), tetrabromobisphenol A, and hexabromocyclododecane, have been identified as potential endocrine disruptors. Quantitative structure-activity relationship models were built based on the in vitro potencies of 26 selected BFRs. The in vitro assays included interactions with, for example, androgen, progesterone, estrogen, and dioxin (aryl hydrocarbon) receptor, plus competition with thyroxine for its plasma carrier protein (transthyretin), inhibition of estradiol sulfation via sulfotransferase, and finally, rate of metabolization. The QSAR modeling, a number of physicochemical parameters were calculated describing the electronic, lipophilic, and structural characteristics of the molecules. These include frontier molecular orbitals, molecular charges, polarities, log octanol/water partitioning coefficient, and two- and three-dimensional molecularproperties. Experimental properties were included and measured for PBDEs, such as their individual ultraviolet spectra (200-320 nm) and retention times on three different high-performance liquid chromatography columns and one nonpolar gas chromatography column. Quantitative structure-activity relationship models based on androgen antagonism and metabolic degradation rates generally gave similar results, suggesting that lower-brominated PBDEs with bromine substitutions in ortho positions and bromine-free meta- and para positions had the highest potencies and metabolic degradation rates. Predictions made for the constituents of the technical flame retardant Bromkal 70-5DE found BDE 17 to be a potent androgen antagonist and BDE 66, which is a relevant PBDE in environmental samples, to be only a weak antagonist.

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.

Listeria monocytogenes in Retail Delicatessens: an Interagency Risk Assessment-model and baseline results.

PubMed

Pouillot, Régis; Gallagher, Daniel; Tang, Jia; Hoelzer, Karin; Kause, Janell; Dennis, Sherri B

2015-01-01

The Interagency Risk Assessment-Listeria monocytogenes (Lm) in Retail Delicatessens provides a scientific assessment of the risk of listeriosis associated with the consumption of ready-to-eat (RTE) foods commonly prepared and sold in the delicatessen (deli) of a retail food store. The quantitative risk assessment (QRA) model simulates the behavior of retail employees in a deli department and tracks the Lm potentially present in this environment and in the food. Bacterial growth, bacterial inactivation (following washing and sanitizing actions), and cross-contamination (from object to object, from food to object, or from object to food) are evaluated through a discrete event modeling approach. The QRA evaluates the risk per serving of deli-prepared RTE food for the susceptible and general population, using a dose-response model from the literature. This QRA considers six separate retail baseline conditions and provides information on the predicted risk of listeriosis for each. Among the baseline conditions considered, the model predicts that (i) retail delis without an environmental source of Lm (such as niches), retail delis without niches that do apply temperature control, and retail delis with niches that do apply temperature control lead to lower predicted risk of listeriosis relative to retail delis with niches and (ii) retail delis with incoming RTE foods that are contaminated with Lm lead to higher predicted risk of listeriosis, directly or through cross-contamination, whether the contaminated incoming product supports growth or not. The risk assessment predicts that listeriosis cases associated with retail delicatessens result from a sequence of key events: (i) the contaminated RTE food supports Lm growth; (ii) improper retail and/or consumer storage temperature or handling results in the growth of Lm on the RTE food; and (iii) the consumer of this RTE food is susceptible to listeriosis. The risk assessment model, therefore, predicts that cross-contamination with Lm at retail predominantly results in sporadic cases.

STakeholder-Objective Risk Model (STORM): Determining the aggregated risk of multiple contaminant hazards in groundwater well catchments

NASA Astrophysics Data System (ADS)

Enzenhoefer, R.; Binning, P. J.; Nowak, W.

2015-09-01

Risk is often defined as the product of probability, vulnerability and value. Drinking water supply from groundwater abstraction is often at risk due to multiple hazardous land use activities in the well catchment. Each hazard might or might not introduce contaminants into the subsurface at any point in time, which then affects the pumped quality upon transport through the aquifer. In such situations, estimating the overall risk is not trivial, and three key questions emerge: (1) How to aggregate the impacts from different contaminants and spill locations to an overall, cumulative impact on the value at risk? (2) How to properly account for the stochastic nature of spill events when converting the aggregated impact to a risk estimate? (3) How will the overall risk and subsequent decision making depend on stakeholder objectives, where stakeholder objectives refer to the values at risk, risk attitudes and risk metrics that can vary between stakeholders. In this study, we provide a STakeholder-Objective Risk Model (STORM) for assessing the total aggregated risk. Or concept is a quantitative, probabilistic and modular framework for simulation-based risk estimation. It rests on the source-pathway-receptor concept, mass-discharge-based aggregation of stochastically occuring spill events, accounts for uncertainties in the involved flow and transport models through Monte Carlo simulation, and can address different stakeholder objectives. We illustrate the application of STORM in a numerical test case inspired by a German drinking water catchment. As one may expect, the results depend strongly on the chosen stakeholder objectives, but they are equally sensitive to different approaches for risk aggregation across different hazards, contaminant types, and over time.

Ash Emissions and Risk Management in the Pacific Ocean

NASA Astrophysics Data System (ADS)

Steensen, T. S.; Webley, P. W.; Stuefer, M.

2012-12-01

Located in the 'Ring of Fire', regions and communities around the Pacific Ocean often face volcanic eruptions and subsequent ash emissions. Volcanic ash clouds pose a significant risk to aviation, especially in the highly-frequented flight corridors around active volcano zones like Indonesia or Eastern Russia and the Alaskan Aleutian Islands. To mitigate and manage such events, a detailed quantitative analysis using a range of scientific measurements, including satellite data and Volcanic Ash Transport and Dispersion (VATD) model results, needs to be conducted in real-time. For the case study of the Sarychev Peak eruption in Russia's Kurile Islands during 2009, we compare ash loading and dispersion from Weather Research and Forecast model with online Chemistry (WRF-Chem) results with satellite data of the eruption. These parameters are needed for the real-time management of volcanic crises to outline no-fly zones and to predict the areas that the ash is most likely to reach in the near future. In the early stages after the eruption, an international group with representatives from the Kamchatkan and Sachalin Volcanic Eruption Response Teams (KVERT, SVERT), the National Aeronautics and Space Administration (NASA), and the Alaska Volcano Observatory (AVO) published early research on the geological and geophysical characteristics of the eruption and the behavior of the resulting ash clouds. The study presented here is a follow-up project aimed to implement VATD model results and satellite data retrospectively to demonstrate the possibilities to develop this approach in real-time for future eruptions. Our research finds that, although meteorological cloud coverage is high in those geographical regions and, consequently, these clouds can cover most of the ash clouds and as such prevent satellites from detecting it, both approaches compare well and supplement each other to reduce the risk of volcanic eruptions. We carry out spatial extent and absolute quantitative comparisons and analyze the sensitivity of model inputs, such as eruption rate and vertical particle size distributions. Our analysis shows that comparisons between real-time satellite observations and VATD model simulations is a complex and difficult process and we present several methods that could be used to reduce the hazards and be useful in any risk assessments.

The application of numerical debris flow modelling for the generation of physical vulnerability curves

NASA Astrophysics Data System (ADS)

Luna, B. Quan; Blahut, J.; van Westen, C. J.; Sterlacchini, S.; van Asch, T. W. J.; Akbas, S. O.

2011-07-01

For a quantitative assessment of debris flow risk, it is essential to consider not only the hazardous process itself but also to perform an analysis of its consequences. This should include the estimation of the expected monetary losses as the product of the hazard with a given magnitude and the vulnerability of the elements exposed. A quantifiable integrated approach of both hazard and vulnerability is becoming a required practice in risk reduction management. This study aims at developing physical vulnerability curves for debris flows through the use of a dynamic run-out model. Dynamic run-out models for debris flows are able to calculate physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk could suffer an impact. These results can then be applied to consequence analyses and risk calculations. On 13 July 2008, after more than two days of intense rainfall, several debris and mud flows were released in the central part of the Valtellina Valley (Lombardy Region, Northern Italy). One of the largest debris flows events occurred in a village called Selvetta. The debris flow event was reconstructed after extensive field work and interviews with local inhabitants and civil protection teams. The Selvetta event was modelled with the FLO-2D program, an Eulerian formulation with a finite differences numerical scheme that requires the specification of an input hydrograph. The internal stresses are isotropic and the basal shear stresses are calculated using a quadratic model. The behaviour and run-out of the flow was reconstructed. The significance of calculated values of the flow depth, velocity, and pressure were investigated in terms of the resulting damage to the affected buildings. The physical damage was quantified for each affected structure within the context of physical vulnerability, which was calculated as the ratio between the monetary loss and the reconstruction value. Three different empirical vulnerability curves were obtained, which are functions of debris flow depth, impact pressure, and kinematic viscosity, respectively. A quantitative approach to estimate the vulnerability of an exposed element to a debris flow which can be independent of the temporal occurrence of the hazard event is presented.

Prioritizing Risks and Uncertainties from Intentional Release of Selected Category A Pathogens

PubMed Central

Hong, Tao; Gurian, Patrick L.; Huang, Yin; Haas, Charles N.

2012-01-01

This paper synthesizes available information on five Category A pathogens (Bacillus anthracis, Yersinia pestis, Francisella tularensis, Variola major and Lassa) to develop quantitative guidelines for how environmental pathogen concentrations may be related to human health risk in an indoor environment. An integrated model of environmental transport and human health exposure to biological pathogens is constructed which 1) includes the effects of environmental attenuation, 2) considers fomite contact exposure as well as inhalational exposure, and 3) includes an uncertainty analysis to identify key input uncertainties, which may inform future research directions. The findings provide a framework for developing the many different environmental standards that are needed for making risk-informed response decisions, such as when prophylactic antibiotics should be distributed, and whether or not a contaminated area should be cleaned up. The approach is based on the assumption of uniform mixing in environmental compartments and is thus applicable to areas sufficiently removed in time and space from the initial release that mixing has produced relatively uniform concentrations. Results indicate that when pathogens are released into the air, risk from inhalation is the main component of the overall risk, while risk from ingestion (dermal contact for B. anthracis) is the main component of the overall risk when pathogens are present on surfaces. Concentrations sampled from untracked floor, walls and the filter of heating ventilation and air conditioning (HVAC) system are proposed as indicators of previous exposure risk, while samples taken from touched surfaces are proposed as indicators of future risk if the building is reoccupied. A Monte Carlo uncertainty analysis is conducted and input-output correlations used to identify important parameter uncertainties. An approach is proposed for integrating these quantitative assessments of parameter uncertainty with broader, qualitative considerations to identify future research priorities. PMID:22412915

Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach.

PubMed

Wijesiri, Buddhi; Deilami, Kaveh; McGree, James; Goonetilleke, Ashantha

2018-02-01

Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health. Copyright © 2017 Elsevier Ltd. All rights reserved.

76 FR 50904 - Thiamethoxam; Pesticide Tolerances

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-17

... exposure and risk. A separate assessment was done for clothianidin. i. Acute exposure. Quantitative acute... not expected to pose a cancer risk, a quantitative dietary exposure assessment for the purposes of...-dietary sources of post application exposure to obtain an estimate of potential combined exposure. These...

Quantitative meta-analytic approaches for the analysis of animal toxicology and epidemiologic data in human health risk assessments

EPA Science Inventory

Often, human health risk assessments have relied on qualitative approaches for hazard identification to integrate evidence across multiple studies to conclude whether particular hazards exist. However, quantitative approaches for evidence integration, including the application o...

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space.

PubMed

Dockendorf, Marissa F; Vargo, Ryan C; Gheyas, Ferdous; Chain, Anne S Y; Chatterjee, Manash S; Wenning, Larissa A

2018-06-01

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.

Religion and Spirituality's Influences on HIV Syndemics Among MSM: A Systematic Review and Conceptual Model.

PubMed

Lassiter, Jonathan M; Parsons, Jeffrey T

2016-02-01

This paper presents a systematic review of the quantitative HIV research that assessed the relationships between religion, spirituality, HIV syndemics, and individual HIV syndemics-related health conditions (e.g. depression, substance abuse, HIV risk) among men who have sex with men (MSM) in the United States. No quantitative studies were found that assessed the relationships between HIV syndemics, religion, and spirituality. Nine studies, with 13 statistical analyses, were found that examined the relationships between individual HIV syndemics-related health conditions, religion, and spirituality. Among the 13 analyses, religion and spirituality were found to have mixed relationships with HIV syndemics-related health conditions (6 nonsignificant associations; 5 negative associations; 2 positive associations). Given the overall lack of inclusion of religion and spirituality in HIV syndemics research, a conceptual model that hypothesizes the potential interactions of religion and spirituality with HIV syndemics-related health conditions is presented. The implications of the model for MSM's health are outlined.

Religion and Spirituality’s Influences on HIV Syndemics Among MSM: A Systematic Review and Conceptual Model

PubMed Central

Parsons, Jeffrey T.

2015-01-01

This paper presents a systematic review of the quantitative HIV research that assessed the relationships between religion, spirituality, HIV syndemics, and individual HIV syndemics-related health conditions (e.g. depression, substance abuse, HIV risk) among men who have sex with men (MSM) in the United States. No quantitative studies were found that assessed the relationships between HIV syndemics, religion, and spirituality. Nine studies, with 13 statistical analyses, were found that examined the relationships between individual HIV syndemics-related health conditions, religion, and spirituality. Among the 13 analyses, religion and spirituality were found to have mixed relationships with HIV syndemics-related health conditions (6 nonsignificant associations; 5 negative associations; 2 positive associations). Given the overall lack of inclusion of religion and spirituality in HIV syndemics research, a conceptual model that hypothesizes the potential interactions of religion and spirituality with HIV syndemics-related health conditions is presented. The implications of the model for MSM’s health are outlined. PMID:26319130

Quantitative landslide risk assessment and mapping on the basis of recent occurrences

NASA Astrophysics Data System (ADS)

Remondo, Juan; Bonachea, Jaime; Cendrero, Antonio

A quantitative procedure for mapping landslide risk is developed from considerations of hazard, vulnerability and valuation of exposed elements. The approach based on former work by the authors, is applied in the Bajo Deba area (northern Spain) where a detailed study of landslide occurrence and damage in the recent past (last 50 years) was carried out. Analyses and mapping are implemented in a Geographic Information System (GIS). The method is based on a susceptibility model developed previously from statistical relationships between past landslides and terrain parameters related to instability. Extrapolations based on past landslide behaviour were used to calculate failure frequency for the next 50 years. A detailed inventory of direct damage due to landslides during the study period was carried out and the main elements at risk in the area identified and mapped. Past direct (monetary) losses per type of element were estimated and expressed as an average 'specific loss' for events of a given magnitude (corresponding to a specified scenario). Vulnerability was assessed by comparing losses with the actual value of the elements affected and expressed as a fraction of that value (0-1). From hazard, vulnerability and monetary value, risk was computed for each element considered. Direct risk maps (€/pixel/year) were obtained and indirect losses from the disruption of economic activities due to landslides assessed. The final result is a risk map and table combining all losses per pixel for a 50-year period. Total monetary value at risk for the Bajo Deba area in the next 50 years is about 2.4 × 10 6 Euros.

Effects of uncertainty and variability on population declines and IUCN Red List classifications.

PubMed

Rueda-Cediel, Pamela; Anderson, Kurt E; Regan, Tracey J; Regan, Helen M

2018-01-22

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories. © 2018 Society for Conservation Biology.

Statistical surrogate models for prediction of high-consequence climate change.

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

Constantine, Paul; Field, Richard V., Jr.; Boslough, Mark Bruce Elrick

2011-09-01

In safety engineering, performance metrics are defined using probabilistic risk assessments focused on the low-probability, high-consequence tail of the distribution of possible events, as opposed to best estimates based on central tendencies. We frame the climate change problem and its associated risks in a similar manner. To properly explore the tails of the distribution requires extensive sampling, which is not possible with existing coupled atmospheric models due to the high computational cost of each simulation. We therefore propose the use of specialized statistical surrogate models (SSMs) for the purpose of exploring the probability law of various climate variables of interest.more » A SSM is different than a deterministic surrogate model in that it represents each climate variable of interest as a space/time random field. The SSM can be calibrated to available spatial and temporal data from existing climate databases, e.g., the Program for Climate Model Diagnosis and Intercomparison (PCMDI), or to a collection of outputs from a General Circulation Model (GCM), e.g., the Community Earth System Model (CESM) and its predecessors. Because of its reduced size and complexity, the realization of a large number of independent model outputs from a SSM becomes computationally straightforward, so that quantifying the risk associated with low-probability, high-consequence climate events becomes feasible. A Bayesian framework is developed to provide quantitative measures of confidence, via Bayesian credible intervals, in the use of the proposed approach to assess these risks.« less

Predicting Team Performance through Human Behavioral Sensing and Quantitative Workflow Instrumentation

DTIC Science & Technology

2016-07-27

make risk-informed decisions during serious games . Statistical models of intra- game performance were developed to determine whether behaviors in...specific facets of the gameplay workflow were predictive of analytical performance and games outcomes. A study of over seventy instrumented teams revealed...more accurate game decisions. 2 Keywords: Humatics · Serious Games · Human-System Interaction · Instrumentation · Teamwork · Communication Analysis

Perceptions of drinking water quality and risk and its effect on behaviour: a cross-national study.

PubMed

Doria, Miguel de França; Pidgeon, Nick; Hunter, Paul R

2009-10-15

There is a growing effort to provide drinking water that has the trust of consumers, but the processes underlying the perception of drinking water quality and risks are still not fully understood. This paper intends to explore the factors involved in public perception of the quality and risks of drinking water. This purpose was addressed with a cross-national mixed-method approach, based on quantitative (survey) and qualitative (focus groups) data collected in the UK and Portugal. The data were analysed using several methods, including structural equation models and generalised linear models. Results suggest that perceptions of water quality and risk result from a complex interaction of diverse factors. The estimation of water quality is mostly influenced by satisfaction with organoleptic properties (especially flavour), risk perception, contextual cues, and perceptions of chemicals (lead, chlorine, and hardness). Risk perception is influenced by organoleptics, perceived water chemicals, external information, past health problems, and trust in water suppliers, among other factors. The use of tap and bottled water to drink was relatively well explained by regression analysis. Several cross-national differences were found and the implications are discussed. Suggestions for future research are provided.

Development of a General Form CO 2 and Brine Flux Input Model

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

Mansoor, K.; Sun, Y.; Carroll, S.

2014-08-01

The National Risk Assessment Partnership (NRAP) project is developing a science-based toolset for the quantitative analysis of the potential risks associated with changes in groundwater chemistry from CO 2 injection. In order to address uncertainty probabilistically, NRAP is developing efficient, reduced-order models (ROMs) as part of its approach. These ROMs are built from detailed, physics-based process models to provide confidence in the predictions over a range of conditions. The ROMs are designed to reproduce accurately the predictions from the computationally intensive process models at a fraction of the computational time, thereby allowing the utilization of Monte Carlo methods to probemore » variability in key parameters. This report presents the procedures used to develop a generalized model for CO 2 and brine leakage fluxes based on the output of a numerical wellbore simulation. The resulting generalized parameters and ranges reported here will be used for the development of third-generation groundwater ROMs.« less

A framework for developing objective and measurable recovery criteria for threatened and endangered species.

PubMed

Himes Boor, Gina K

2014-02-01

For species listed under the U.S. Endangered Species Act (ESA), the U.S. Fish and Wildlife Service and National Marine Fisheries Service are tasked with writing recovery plans that include "objective, measurable criteria" that define when a species is no longer at risk of extinction, but neither the act itself nor agency guidelines provide an explicit definition of objective, measurable criteria. Past reviews of recovery plans, including one published in 2012, show that many criteria lack quantitative metrics with clear biological rationale and are not meeting the measureable and objective mandate. I reviewed how objective, measureable criteria have been defined implicitly and explicitly in peer-reviewed literature, the ESA, other U.S. statutes, and legal decisions. Based on a synthesis of these sources, I propose the following 6 standards be used as minimum requirements for objective, measurable criteria: contain a quantitative threshold with calculable units, stipulate a timeframe over which they must be met, explicitly define the spatial extent or population to which they apply, specify a sampling procedure that includes sample size, specify a statistical significance level, and include justification by providing scientific evidence that the criteria define a species whose extinction risk has been reduced to the desired level. To meet these 6 standards, I suggest that recovery plans be explicitly guided by and organized around a population viability modeling framework even if data or agency resources are too limited to complete a viability model. When data and resources are available, recovery criteria can be developed from the population viability model results, but when data and resources are insufficient for model implementation, extinction risk thresholds can be used as criteria. A recovery-planning approach centered on viability modeling will also yield appropriately focused data-acquisition and monitoring plans and will facilitate a seamless transition from recovery planning to delisting. © 2013 Society for Conservation Biology.

Assessing the impact of the Lebanese National Polio Immunization Campaign using a population-based computational model.

PubMed

Alawieh, Ali; Sabra, Zahraa; Langley, E Farris; Bizri, Abdul Rahman; Hamadeh, Randa; Zaraket, Fadi A

2017-11-25

After the re-introduction of poliovirus to Syria in 2013, Lebanon was considered at high transmission risk due to its proximity to Syria and the high number of Syrian refugees. However, after a large-scale national immunization initiative, Lebanon was able to prevent a potential outbreak of polio among nationals and refugees. In this work, we used a computational individual-simulation model to assess the risk of poliovirus threat to Lebanon prior and after the immunization campaign and to quantitatively assess the healthcare impact of the campaign and the required standards that need to be maintained nationally to prevent a future outbreak. Acute poliomyelitis surveillance in Lebanon was along with the design and coverage rate of the recent national polio immunization campaign were reviewed from the records of the Lebanese Ministry of Public Health. Lebanese population demographics including Syrian and Palestinian refugees were reviewed to design individual-based models that predicts the consequences of polio spread to Lebanon and evaluate the outcome of immunization campaigns. The model takes into account geographic, demographic and health-related features. Our simulations confirmed the high risk of polio outbreaks in Lebanon within 10 days of case introduction prior to the immunization campaign, and showed that the current immunization campaign significantly reduced the speed of the infection in the event poliomyelitis cases enter the country. A minimum of 90% national immunization coverage was found to be required to prevent exponential propagation of potential transmission. Both surveillance and immunization efforts should be maintained at high standards in Lebanon and other countries in the area to detect and limit any potential outbreak. The use of computational population simulation models can provide a quantitative approach to assess the impact of immunization campaigns and the burden of infectious diseases even in the context of population migration.

Knowledge gaps in host-parasite interaction preclude accurate assessment of meat-borne exposure to Toxoplasma gondii.

PubMed

Crotta, M; Limon, G; Blake, D P; Guitian, J

2017-11-16

Toxoplasma gondii is recognized as a widely prevalent zoonotic parasite worldwide. Although several studies clearly identified meat products as an important source of T. gondii infections in humans, quantitative understanding of the risk posed to humans through the food chain is surprisingly scant. While probabilistic risk assessments for pathogens such as Campylobacter jejuni, Listeria monocytogenes or Escherichia coli have been well established, attempts to quantify the probability of human exposure to T. gondii through consumption of food products of animal origin are at early stages. The biological complexity of the life cycle of T. gondii and limited understanding of several fundamental aspects of the host/parasite interaction, require the adoption of numerous critical assumptions and significant simplifications. In this study, we present a hypothetical quantitative model for the assessment of human exposure to T. gondii through meat products. The model has been conceptualized to capture the dynamics leading to the presence of parasite in meat and, for illustrative purposes, used to estimate the probability of at least one viable cyst occurring in 100g of fresh pork meat in England. Available data, including the results of a serological survey in pigs raised in England were used as a starting point to implement a probabilistic model and assess the fate of the parasite along the food chain. Uncertainty distributions were included to describe and account for the lack of knowledge where necessary. To quantify the impact of the key model inputs, sensitivity and scenario analyses were performed. The overall probability of 100g of a hypothetical edible tissue containing at least 1 cyst was 5.54%. Sensitivity analysis indicated that the variables exerting the greater effect on the output mean were the number of cysts and number of bradyzoites per cyst. Under the best and the worst scenarios, the probability of a single portion of fresh pork meat containing at least 1 viable cyst resulted 1.14% and 9.97% indicating that the uncertainty and lack of data surrounding key input parameters of the model preclude accurate estimation of T. gondii exposure through consumption of meat products. The hypothetical model conceptualized here is coherent with current knowledge of the biology of the parasite. Simulation outputs clearly identify the key gaps in our knowledge of the host-parasite interaction that, when filled, will support quantitative assessments and much needed accurate estimates of the risk of human exposure. Copyright © 2017 Elsevier B.V. All rights reserved.