75 FR 80544 - NUREG-1953, Confirmatory Thermal-Hydraulic Analysis To Support Specific Success Criteria in the...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-22

... To Support Specific Success Criteria in the Standardized Plant Analysis Risk Models--Surry and Peach... Specific Success Criteria in the Standardized Plant Analysis Risk Models--Surry and Peach Bottom, Draft..., ``Confirmatory Thermal-Hydraulic Analysis to Support Specific Success Criteria in the Standardized Plant Analysis...

Methylenetetrahydrofolate reductase gene polymorphism and risk of chronic myelogenous leukemia: a meta-analysis.

PubMed

Li, Chen; Yichao, Jin; Jiaxin, Lin; Yueting, Zhang; Qin, Lu; Tonghua, Yang

2015-01-01

Reported evidence supports a role for methylenetetrahydrofolate reductase (MTHFR) in the risk of chronic myelogenous leykemia (CML). However, these reports arrived at non-conclusive and even conflicting results regarding the association between two common MTHFR polymorphisms (C677T and A1298C) and CML risk. Thus, a meta-analysis was carried out to clarify a more precise association between these two polymorphisms and the CML risk by updating the available publications. Pooled odds ratios (OR) with corresponding 95% confidence interval (95% CI) and stratification analysis were performed to estimate the relationship between MTHFR polymorphisms and the risk of CML under different genetic comparison models. Data from the meta-analysis showed no significant association between MTHFR C677T polymorphism and CML risk. However, significant associations were found between MTHFR A1298C variants and CML risk under homozygous comparison model (CC vs AA, OR=1.62, 95% CI=1.11-2.36, p=0.01) and dominant comparison model (CC+AC vs AA, OR=1.68, 95% CI=1.17-2.43, p=0.005) in overall population; especially more obvious impacts were noticed for Asian populations in subgroup analysis for homozygous model (CC vs AA, OR=2.00, 95% CI=1.25-3.21, p=0.004) and dominant model (CC+AC vs AA, OR=2.49, 95% CI=1.42-4.36, p=0.001), but this did not apply in Caucasian populations. The results of this meta-analysis suggested no significant association between MTHFR C677T polymorphism and CML risk, while an increased CML risk was noticed for 1298C variant carriers, especially in Asian populations but not in Caucasian populations, which suggested ethnicity differences between MTHFR A1298C polymorphisms and risk of CML.

Analysis of dengue fever risk using geostatistics model in bone regency

NASA Astrophysics Data System (ADS)

Amran, Stang, Mallongi, Anwar

2017-03-01

This research aim is to analysis of dengue fever risk based on Geostatistics model in Bone Regency. Risk levels of dengue fever are denoted by parameter of Binomial distribution. Effect of temperature, rainfalls, elevation, and larvae abundance are investigated through Geostatistics model. Bayesian hierarchical method is used in estimation process. Using dengue fever data in eleven locations this research shows that temperature and rainfall have significant effect of dengue fever risk in Bone regency.

Latent Model Analysis of Substance Use and HIV Risk Behaviors among High-Risk Minority Adults

ERIC Educational Resources Information Center

Wang, Min Qi; Matthew, Resa F.; Chiu, Yu-Wen; Yan, Fang; Bellamy, Nikki D.

2007-01-01

Objectives: This study evaluated substance use and HIV risk profile using a latent model analysis based on ecological theory, inclusive of a risk and protective factor framework, in sexually active minority adults (N=1,056) who participated in a federally funded substance abuse and HIV prevention health initiative from 2002 to 2006. Methods: Data…

Relationship between US Societal Fatality Risk per Vehicle Miles of Travel and Mass, for Individual Vehicle Models over Time (Model Year)

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

Wenzel, Tom P.

This report presents a new approach to analyze the relationship between vehicle mass and risk: tracking fatality risk by vehicle model year and mass, for individual vehicle models. This approach is appealing as it greatly minimizes the influence of driver characteristics and behavior, and crash circumstances, on fatality risk. However, only the most popular vehicle models, with the largest number of fatalities, can be analyzed in this manner. While the analysis of all vehicle models of a given type suggests that there is a relationship between increased mass and fatality risk, analysis of the ten most popular four-door car modelsmore » separately suggests that this relationship is weak: in many cases when the mass of a specific vehicle model is increased societal fatality risk is unchanged or even increases. These results suggest that increasing the mass of an individual vehicle model does not necessarily lead to decreased societal fatality risk.« less

Competing risk models in reliability systems, an exponential distribution model with Bayesian analysis approach

NASA Astrophysics Data System (ADS)

Iskandar, I.

2018-03-01

The exponential distribution is the most widely used reliability analysis. This distribution is very suitable for representing the lengths of life of many cases and is available in a simple statistical form. The characteristic of this distribution is a constant hazard rate. The exponential distribution is the lower rank of the Weibull distributions. In this paper our effort is to introduce the basic notions that constitute an exponential competing risks model in reliability analysis using Bayesian analysis approach and presenting their analytic methods. The cases are limited to the models with independent causes of failure. A non-informative prior distribution is used in our analysis. This model describes the likelihood function and follows with the description of the posterior function and the estimations of the point, interval, hazard function, and reliability. The net probability of failure if only one specific risk is present, crude probability of failure due to a specific risk in the presence of other causes, and partial crude probabilities are also included.

The RTEL1 rs6010620 polymorphism and glioma risk: a meta-analysis based on 12 case-control studies.

PubMed

Du, Shu-Li; Geng, Ting-Ting; Feng, Tian; Chen, Cui-Ping; Jin, Tian-Bo; Chen, Chao

2014-01-01

The association between the RTEL1 rs6010620 single nucleotide polymorphism (SNP) and glioma risk has been extensively studied. However, the results remain inconclusive. To further examine this association, we performed a meta-analysis. A computerized search of the PubMed and Embase databases for publications regarding the RTEL1 rs6010620 polymorphism and glioma cancer risk was performed. Genotype data were analyzed in a meta-analysis. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated to assess the association. Sensitivity analyses, tests of heterogeneity, cumulative meta-analyses, and assessments of bias were performed in our meta-analysis. Our meta-analysis confirmed that risk with allele A is lower than with allele G for glioma. The A allele of rs6010620 in RTEL1 decreased the risk of developing glioma in the 12 case-control studies for all genetic models: the allele model (OR=0.752, 95%CI: 0.715-0.792), the dominant model (OR=0.729, 95%CI: 0.685-0.776), the recessive model (OR=0.647, 95%CI: 0.569-0.734), the homozygote comparison (OR=0.528, 95%CI: 0.456-0.612), and the heterozygote comparison (OR=0.761, 95%CI: 0.713-0.812). In all genetic models, the association between the RTEL1 rs6010620 polymorphism and glioma risk was significant. This meta-analysis suggests that the RTEL1 rs6010620 polymorphism may be a risk factor for glioma. Further functional studies evaluating this polymorphism and glioma risk are warranted.

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.

Is risk analysis scientific?

PubMed

Hansson, Sven Ove; Aven, Terje

2014-07-01

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

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.

Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis.

PubMed

Gray, Ewan; Donten, Anna; Karssemeijer, Nico; van Gils, Carla; Evans, D Gareth; Astley, Sue; Payne, Katherine

2017-09-01

To identify the incremental costs and consequences of stratified national breast screening programs (stratified NBSPs) and drivers of relative cost-effectiveness. A decision-analytic model (discrete event simulation) was conceptualized to represent four stratified NBSPs (risk 1, risk 2, masking [supplemental screening for women with higher breast density], and masking and risk 1) compared with the current UK NBSP and no screening. The model assumed a lifetime horizon, the health service perspective to identify costs (£, 2015), and measured consequences in quality-adjusted life-years (QALYs). Multiple data sources were used: systematic reviews of effectiveness and utility, published studies reporting costs, and cohort studies embedded in existing NBSPs. Model parameter uncertainty was assessed using probabilistic sensitivity analysis and one-way sensitivity analysis. The base-case analysis, supported by probabilistic sensitivity analysis, suggested that the risk stratified NBSPs (risk 1 and risk-2) were relatively cost-effective when compared with the current UK NBSP, with incremental cost-effectiveness ratios of £16,689 per QALY and £23,924 per QALY, respectively. Stratified NBSP including masking approaches (supplemental screening for women with higher breast density) was not a cost-effective alternative, with incremental cost-effectiveness ratios of £212,947 per QALY (masking) and £75,254 per QALY (risk 1 and masking). When compared with no screening, all stratified NBSPs could be considered cost-effective. Key drivers of cost-effectiveness were discount rate, natural history model parameters, mammographic sensitivity, and biopsy rates for recalled cases. A key assumption was that the risk model used in the stratification process was perfectly calibrated to the population. This early model-based cost-effectiveness analysis provides indicative evidence for decision makers to understand the key drivers of costs and QALYs for exemplar stratified NBSP. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

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.

The role of building models in the evaluation of heat-related risks

NASA Astrophysics Data System (ADS)

Buchin, Oliver; Jänicke, Britta; Meier, Fred; Scherer, Dieter; Ziegler, Felix

2016-04-01

Hazard-risk relationships in epidemiological studies are generally based on the outdoor climate, despite the fact that most of humans' lifetime is spent indoors. By coupling indoor and outdoor climates with a building model, the risk concept developed can still be based on the outdoor conditions but also includes exposure to the indoor climate. The influence of non-linear building physics and the impact of air conditioning on heat-related risks can be assessed in a plausible manner using this risk concept. For proof of concept, the proposed risk concept is compared to a traditional risk analysis. As an example, daily and city-wide mortality data of the age group 65 and older in Berlin, Germany, for the years 2001-2010 are used. Four building models with differing complexity are applied in a time-series regression analysis. This study shows that indoor hazard better explains the variability in the risk data compared to outdoor hazard, depending on the kind of building model. Simplified parameter models include the main non-linear effects and are proposed for the time-series analysis. The concept shows that the definitions of heat events, lag days, and acclimatization in a traditional hazard-risk relationship are influenced by the characteristics of the prevailing building stock.

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

A simple prognostic model for overall survival in metastatic renal cell carcinoma

PubMed Central

Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

Tutorial: Parallel Computing of Simulation Models for Risk Analysis.

PubMed

Reilly, Allison C; Staid, Andrea; Gao, Michael; Guikema, Seth D

2016-10-01

Simulation models are widely used in risk analysis to study the effects of uncertainties on outcomes of interest in complex problems. Often, these models are computationally complex and time consuming to run. This latter point may be at odds with time-sensitive evaluations or may limit the number of parameters that are considered. In this article, we give an introductory tutorial focused on parallelizing simulation code to better leverage modern computing hardware, enabling risk analysts to better utilize simulation-based methods for quantifying uncertainty in practice. This article is aimed primarily at risk analysts who use simulation methods but do not yet utilize parallelization to decrease the computational burden of these models. The discussion is focused on conceptual aspects of embarrassingly parallel computer code and software considerations. Two complementary examples are shown using the languages MATLAB and R. A brief discussion of hardware considerations is located in the Appendix. © 2016 Society for Risk Analysis.

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.

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.

[Survival analysis with competing risks: estimating failure probability].

PubMed

Llorca, Javier; Delgado-Rodríguez, Miguel

2004-01-01

To show the impact of competing risks of death on survival analysis. We provide an example of survival time without chronic rejection after heart transplantation, where death before rejection acts as a competing risk. Using a computer simulation, we compare the Kaplan-Meier estimator and the multiple decrement model. The Kaplan-Meier method overestimated the probability of rejection. Next, we illustrate the use of the multiple decrement model to analyze secondary end points (in our example: death after rejection). Finally, we discuss Kaplan-Meier assumptions and why they fail in the presence of competing risks. Survival analysis should be adjusted for competing risks of death to avoid overestimation of the risk of rejection produced with the Kaplan-Meier method.

Development of Rock Engineering Systems-Based Models for Flyrock Risk Analysis and Prediction of Flyrock Distance in Surface Blasting

NASA Astrophysics Data System (ADS)

Faramarzi, Farhad; Mansouri, Hamid; Farsangi, Mohammad Ali Ebrahimi

2014-07-01

The environmental effects of blasting must be controlled in order to comply with regulatory limits. Because of safety concerns and risk of damage to infrastructures, equipment, and property, and also having a good fragmentation, flyrock control is crucial in blasting operations. If measures to decrease flyrock are taken, then the flyrock distance would be limited, and, in return, the risk of damage can be reduced or eliminated. This paper deals with modeling the level of risk associated with flyrock and, also, flyrock distance prediction based on the rock engineering systems (RES) methodology. In the proposed models, 13 effective parameters on flyrock due to blasting are considered as inputs, and the flyrock distance and associated level of risks as outputs. In selecting input data, the simplicity of measuring input data was taken into account as well. The data for 47 blasts, carried out at the Sungun copper mine, western Iran, were used to predict the level of risk and flyrock distance corresponding to each blast. The obtained results showed that, for the 47 blasts carried out at the Sungun copper mine, the level of estimated risks are mostly in accordance with the measured flyrock distances. Furthermore, a comparison was made between the results of the flyrock distance predictive RES-based model, the multivariate regression analysis model (MVRM), and, also, the dimensional analysis model. For the RES-based model, R 2 and root mean square error (RMSE) are equal to 0.86 and 10.01, respectively, whereas for the MVRM and dimensional analysis, R 2 and RMSE are equal to (0.84 and 12.20) and (0.76 and 13.75), respectively. These achievements confirm the better performance of the RES-based model over the other proposed models.

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.

Modeling Opponents in Adversarial Risk Analysis.

PubMed

Rios Insua, David; Banks, David; Rios, Jesus

2016-04-01

Adversarial risk analysis has been introduced as a framework to deal with risks derived from intentional actions of adversaries. The analysis supports one of the decisionmakers, who must forecast the actions of the other agents. Typically, this forecast must take account of random consequences resulting from the set of selected actions. The solution requires one to model the behavior of the opponents, which entails strategic thinking. The supported agent may face different kinds of opponents, who may use different rationality paradigms, for example, the opponent may behave randomly, or seek a Nash equilibrium, or perform level-k thinking, or use mirroring, or employ prospect theory, among many other possibilities. We describe the appropriate analysis for these situations, and also show how to model the uncertainty about the rationality paradigm used by the opponent through a Bayesian model averaging approach, enabling a fully decision-theoretic solution. We also show how as we observe an opponent's decision behavior, this approach allows learning about the validity of each of the rationality models used to predict his decision by computing the models' (posterior) probabilities, which can be understood as a measure of their validity. We focus on simultaneous decision making by two agents. © 2015 Society for Risk Analysis.

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.

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.

Global Persistent Attack: A Systems Architecture, Process Modeling, and Risk Analysis Approach

DTIC Science & Technology

2008-06-01

develop an analysis process for quantifying risk associated with the limitations presented by a fiscally constrained environment. The second step...previous independent analysis of each force structure provided information for quantifying risk associated with the given force presentations, the

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

Study of a risk-based piping inspection guideline system.

PubMed

Tien, Shiaw-Wen; Hwang, Wen-Tsung; Tsai, Chih-Hung

2007-02-01

A risk-based inspection system and a piping inspection guideline model were developed in this study. The research procedure consists of two parts--the building of a risk-based inspection model for piping and the construction of a risk-based piping inspection guideline model. Field visits at the plant were conducted to develop the risk-based inspection and strategic analysis system. A knowledge-based model had been built in accordance with international standards and local government regulations, and the rational unified process was applied for reducing the discrepancy in the development of the models. The models had been designed to analyze damage factors, damage models, and potential damage positions of piping in the petrochemical plants. The purpose of this study was to provide inspection-related personnel with the optimal planning tools for piping inspections, hence, to enable effective predictions of potential piping risks and to enhance the better degree of safety in plant operations that the petrochemical industries can be expected to achieve. A risk analysis was conducted on the piping system of a petrochemical plant. The outcome indicated that most of the risks resulted from a small number of pipelines.

Deficient Contractor Business Systems: Applying the Value at Risk (VaR) Model to Earned Value Management Systems

DTIC Science & Technology

2013-06-30

QUANTITATIVE RISK ANALYSIS The use of quantitative cost risk analysis tools can be valuable in measuring numerical risk to the government ( Galway , 2004...assessment of the EVMS itself. Galway (2004) practically linked project quantitative risk assessment to EVM by focusing on cost, schedule, and...www.amazon.com Galway , L. (2004, February). Quantitative risk analysis for project management: A critical review (RAND Working Paper WR-112-RC

Global review of open access risk assessment software packages valid for global or continental scale analysis

NASA Astrophysics Data System (ADS)

Daniell, James; Simpson, Alanna; Gunasekara, Rashmin; Baca, Abigail; Schaefer, Andreas; Ishizawa, Oscar; Murnane, Rick; Tijssen, Annegien; Deparday, Vivien; Forni, Marc; Himmelfarb, Anne; Leder, Jan

2015-04-01

Over the past few decades, a plethora of open access software packages for the calculation of earthquake, volcanic, tsunami, storm surge, wind and flood have been produced globally. As part of the World Bank GFDRR Review released at the Understanding Risk 2014 Conference, over 80 such open access risk assessment software packages were examined. Commercial software was not considered in the evaluation. A preliminary analysis was used to determine whether the 80 models were currently supported and if they were open access. This process was used to select a subset of 31 models that include 8 earthquake models, 4 cyclone models, 11 flood models, and 8 storm surge/tsunami models for more detailed analysis. By using multi-criteria analysis (MCDA) and simple descriptions of the software uses, the review allows users to select a few relevant software packages for their own testing and development. The detailed analysis evaluated the models on the basis of over 100 criteria and provides a synopsis of available open access natural hazard risk modelling tools. In addition, volcano software packages have since been added making the compendium of risk software tools in excess of 100. There has been a huge increase in the quality and availability of open access/source software over the past few years. For example, private entities such as Deltares now have an open source policy regarding some flood models (NGHS). In addition, leaders in developing risk models in the public sector, such as Geoscience Australia (EQRM, TCRM, TsuDAT, AnuGA) or CAPRA (ERN-Flood, Hurricane, CRISIS2007 etc.), are launching and/or helping many other initiatives. As we achieve greater interoperability between modelling tools, we will also achieve a future wherein different open source and open access modelling tools will be increasingly connected and adapted towards unified multi-risk model platforms and highly customised solutions. It was seen that many software tools could be improved by enabling user-defined exposure and vulnerability. Without this function, many tools can only be used regionally and not at global or continental scale. It is becoming increasingly easy to use multiple packages for a single region and/or hazard to characterize the uncertainty in the risk, or use as checks for the sensitivities in the analysis. There is a potential for valuable synergy between existing software. A number of open source software packages could be combined to generate a multi-risk model with multiple views of a hazard. This extensive review has simply attempted to provide a platform for dialogue between all open source and open access software packages and to hopefully inspire collaboration between developers, given the great work done by all open access and open source developers.

Command Process Modeling & Risk Analysis

NASA Technical Reports Server (NTRS)

Meshkat, Leila

2011-01-01

Commanding Errors may be caused by a variety of root causes. It's important to understand the relative significance of each of these causes for making institutional investment decisions. One of these causes is the lack of standardized processes and procedures for command and control. We mitigate this problem by building periodic tables and models corresponding to key functions within it. These models include simulation analysis and probabilistic risk assessment models.

SE Great Basin Play Fairway Analysis

DOE Data Explorer

Adam Brandt

2015-11-15

This submission includes a Na/K geothermometer probability greater than 200 deg C map, as well as two play fairway analysis (PFA) models. The probability map acts as a composite risk segment for the PFA models. The PFA models differ in their application of magnetotelluric conductors as composite risk segments. These PFA models map out the geothermal potential in the region of SE Great Basin, Utah.

Ensemble habitat mapping of invasive plant species

USGS Publications Warehouse

Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.

2010-01-01

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.

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

PubMed

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

2016-08-26

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

Developing and validating risk prediction models in an individual participant data meta-analysis

PubMed Central

2014-01-01

Background Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach. Methods A qualitative review of the aims, methodology, and reporting in 15 articles that developed a risk prediction model using IPD from multiple studies. Results The IPD approach offers many opportunities but methodological challenges exist, including: unavailability of requested IPD, missing patient data and predictors, and between-study heterogeneity in methods of measurement, outcome definitions and predictor effects. Most articles develop their model using IPD from all available studies and perform only an internal validation (on the same set of data). Ten of the 15 articles did not allow for any study differences in baseline risk (intercepts), potentially limiting their model’s applicability and performance in some populations. Only two articles used external validation (on different data), including a novel method which develops the model on all but one of the IPD studies, tests performance in the excluded study, and repeats by rotating the omitted study. Conclusions An IPD meta-analysis offers unique opportunities for risk prediction research. Researchers can make more of this by allowing separate model intercept terms for each study (population) to improve generalisability, and by using ‘internal-external cross-validation’ to simultaneously develop and validate their model. Methodological challenges can be reduced by prospectively planned collaborations that share IPD for risk prediction. PMID:24397587

Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees.

PubMed

Shi, K-Q; Zhou, Y-Y; Yan, H-D; Li, H; Wu, F-L; Xie, Y-Y; Braddock, M; Lin, X-Y; Zheng, M-H

2017-02-01

At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification. © 2016 John Wiley & Sons Ltd.

Biomechanical analysis on fracture risk associated with bone deformity

NASA Astrophysics Data System (ADS)

Kamal, Nur Amalina Nadiah Mustafa; Som, Mohd Hanafi Mat; Basaruddin, Khairul Salleh; Daud, Ruslizam

2017-09-01

Osteogenesis Imperfecta (OI) is a disease related to bone deformity and is also known as `brittle bone' disease. Currently, medical personnel predict the bone fracture solely based on their experience. In this study, the prediction for risk of fracture was carried out by using finite element analysis on the simulated OI bone of femur. The main objective of this research was to analyze the fracture risk of OI-affected bone with respect to various loadings. A total of 12 models of OI bone were developed by applying four load cases and the angle of deformation for each of the models was calculated. The models were differentiated into four groups, namely standard, light, mild and severe. The results show that only a small amount of load is required to increase the fracture risk of the bone when the model is tested with hopping conditions. The analysis also shows that the torsional load gives a small effect to the increase of the fracture risk of the bone.

Empirical analysis of farmers' drought risk perception: objective factors, personal circumstances, and social influence.

PubMed

Duinen, Rianne van; Filatova, Tatiana; Geurts, Peter; Veen, Anne van der

2015-04-01

Drought-induced water shortage and salinization are a global threat to agricultural production. With climate change, drought risk is expected to increase as drought events are assumed to occur more frequently and to become more severe. The agricultural sector's adaptive capacity largely depends on farmers' drought risk perceptions. Understanding the formation of farmers' drought risk perceptions is a prerequisite to designing effective and efficient public drought risk management strategies. Various strands of literature point at different factors shaping individual risk perceptions. Economic theory points at objective risk variables, whereas psychology and sociology identify subjective risk variables. This study investigates and compares the contribution of objective and subjective factors in explaining farmers' drought risk perception by means of survey data analysis. Data on risk perceptions, farm characteristics, and various other personality traits were collected from farmers located in the southwest Netherlands. From comparing the explanatory power of objective and subjective risk factors in separate models and a full model of risk perception, it can be concluded that farmers' risk perceptions are shaped by both rational and emotional factors. In a full risk perception model, being located in an area with external water supply, owning fields with salinization issues, cultivating drought-/salt-sensitive crops, farm revenue, drought risk experience, and perceived control are significant explanatory variables of farmers' drought risk perceptions. © 2014 Society for Risk Analysis.

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method.

PubMed

Deng, Xinyang; Jiang, Wen

2017-09-12

Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model.

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method

PubMed Central

Deng, Xinyang

2017-01-01

Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model. PMID:28895905

Modelling tsunami inundation for risk analysis at the Andaman Sea Coast of Thailand

NASA Astrophysics Data System (ADS)

Kaiser, G.; Kortenhaus, A.

2009-04-01

The mega-tsunami of Dec. 26, 2004 strongly impacted the Andaman Sea coast of Thailand and devastated coastal ecosystems as well as towns, settlements and tourism resorts. In addition to the tragic loss of many lives, the destruction or damage of life-supporting infrastructure, such as buildings, roads, water & power supply etc. caused high economic losses in the region. To mitigate future tsunami impacts there is a need to assess the tsunami hazard and vulnerability in flood prone areas at the Andaman Sea coast in order to determine the spatial distribution of risk and to develop risk management strategies. In the bilateral German-Thai project TRAIT research is performed on integrated risk assessment for the Provinces Phang Nga and Phuket in southern Thailand, including a hazard analysis, i.e. modelling tsunami propagation to the coast, tsunami wave breaking and inundation characteristics, as well as vulnerability analysis of the socio-economic and the ecological system in order to determine the scenario-based, specific risk for the region. In this presentation results of the hazard analysis and the inundation simulation are presented and discussed. Numerical modelling of tsunami propagation and inundation simulation is an inevitable tool for risk analysis, risk management and evacuation planning. While numerous investigations have been made to model tsunami wave generation and propagation in the Indian Ocean, there is still a lack in determining detailed inundation patterns, i.e. water depth and flow dynamics. However, for risk management and evacuation planning this knowledge is essential. As the accuracy of the inundation simulation is strongly depending on the available bathymetric and the topographic data, a multi-scale approach is chosen in this work. The ETOPO Global Relief Model as a bathymetric basis and the Shuttle Radar Topography Mission (SRTM90) have been widely applied in tsunami modelling approaches as these data are free and almost world-wide available. However, to model tsunami-induced inundation for risk analysis and management purposes the accuracy of these data is not sufficient as the processes in the near-shore zone cannot be modelled accurately enough and the spatial resolution of the topography is weak. Moreover, the SRTM data provide a digital surface model which includes vegetation and buildings in the surface description. To improve the data basis additional bathymetric data were used in the near shore zone of the Phang Nga and Phuket coastlines and various remote sensing techniques as well as additional GPS measurements were applied to derive a high resolution topography from satellite and airborne data. Land use classifications and filter methods were developed to correct the digital surface models to digital elevation models. Simulations were then performed with a non-linear shallow water model to model the 2004 Asian Tsunami and to simulate possible future ones. Results of water elevation near the coast were compared with field measurements and observations, and the influence of the resolution of the topography on inundation patterns like water depth, velocity, dispersion and duration of the flood were analysed. The inundation simulation provides detailed hazard maps and is considered a reliable basis for risk assessment and risk zone mapping. Results are regarded vital for estimation of tsunami induced damages and evacuation planning. Results of the aforementioned simulations will be discussed during the conference. Differences of the numerical results using topographic data of different scales and modified by different post processing techniques will be analysed and explained. Further use of the results with respect to tsunami risk analysis and management will also be demonstrated.

Using software security analysis to verify the secure socket layer (SSL) protocol

NASA Technical Reports Server (NTRS)

Powell, John D.

2004-01-01

nal Aeronautics and Space Administration (NASA) have tens of thousands of networked computer systems and applications. Software Security vulnerabilities present risks such as lost or corrupted data, information the3, and unavailability of critical systems. These risks represent potentially enormous costs to NASA. The NASA Code Q research initiative 'Reducing Software Security Risk (RSSR) Trough an Integrated Approach '' offers, among its capabilities, formal verification of software security properties, through the use of model based verification (MBV) to address software security risks. [1,2,3,4,5,6] MBV is a formal approach to software assurance that combines analysis of software, via abstract models, with technology, such as model checkers, that provide automation of the mechanical portions of the analysis process. This paper will discuss: The need for formal analysis to assure software systems with respect to software and why testing alone cannot provide it. The means by which MBV with a Flexible Modeling Framework (FMF) accomplishes the necessary analysis task. An example of FMF style MBV in the verification of properties over the Secure Socket Layer (SSL) communication protocol as a demonstration.

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

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 Empirical Assessment of Defense Contractor Risk 1976-1984.

DTIC Science & Technology

1986-06-01

Model to evaluate the. Department of Defense contract pricing , financing, and profit policies . ’ D*’ ’ *NTV D? 7A’:: TA E *A l ..... -:- A-i SN 0102...defense con- tractor risk-return relationship is performed utilizing four methods: mean-variance analysis of rate of return, the Capital Asset Pricing Model ...relationship is performed utilizing four methods: mean- variance analysis of rate of return, the Capital Asset Pricing Model , mean-variance analysis of total

Predicting Risk Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation

ERIC Educational Resources Information Center

Weber, Elke U.; Shafir, Sharoni; Blais, Ann-Renee

2004-01-01

This article examines the statistical determinants of risk preference. In a meta-analysis of animal risk preference (foraging birds and insects), the coefficient of variation (CV), a measure of risk per unit of return, predicts choices far better than outcome variance, the risk measure of normative models. In a meta-analysis of human risk…

Risk analysis based on hazards interactions

NASA Astrophysics Data System (ADS)

Rossi, Lauro; Rudari, Roberto; Trasforini, Eva; De Angeli, Silvia; Becker, Joost

2017-04-01

Despite an increasing need for open, transparent, and credible multi-hazard risk assessment methods, models, and tools, the availability of comprehensive risk information needed to inform disaster risk reduction is limited, and the level of interaction across hazards is not systematically analysed. Risk assessment methodologies for different hazards often produce risk metrics that are not comparable. Hazard interactions (consecutive occurrence two or more different events) are generally neglected, resulting in strongly underestimated risk assessment in the most exposed areas. This study presents cases of interaction between different hazards, showing how subsidence can affect coastal and river flood risk (Jakarta and Bandung, Indonesia) or how flood risk is modified after a seismic event (Italy). The analysis of well documented real study cases, based on a combination between Earth Observation and in-situ data, would serve as basis the formalisation of a multi-hazard methodology, identifying gaps and research frontiers. Multi-hazard risk analysis is performed through the RASOR platform (Rapid Analysis and Spatialisation Of Risk). A scenario-driven query system allow users to simulate future scenarios based on existing and assumed conditions, to compare with historical scenarios, and to model multi-hazard risk both before and during an event (www.rasor.eu).

School Reform for Youth At Risk: Analysis of Six Change Models. Volume I: Summary and Analysis.

ERIC Educational Resources Information Center

McCollum, Heather

This document analyzes six school-reform models for at-risk youth. Part 1 examines three curriculum-based reform programs that explicitly target curriculum and instruction: Reading Recovery; Success for All; and the Academy model. These programs focus on changes in student achievement and work within the structure of existing schools. Part 2…

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.

Application of the NUREG/CR-6850 EPRI/NRC Fire PRA Methodology to a DOE Facility

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

Tom Elicson; Bentley Harwood; Richard Yorg

2011-03-01

The application NUREG/CR-6850 EPRI/NRC fire PRA methodology to DOE facility presented several challenges. This paper documents the process and discusses several insights gained during development of the fire PRA. A brief review of the tasks performed is provided with particular focus on the following: • Tasks 5 and 14: Fire-induced risk model and fire risk quantification. A key lesson learned was to begin model development and quantification as early as possible in the project using screening values and simplified modeling if necessary. • Tasks 3 and 9: Fire PRA cable selection and detailed circuit failure analysis. In retrospect, it wouldmore » have been beneficial to perform the model development and quantification in 2 phases with detailed circuit analysis applied during phase 2. This would have allowed for development of a robust model and quantification earlier in the project and would have provided insights into where to focus the detailed circuit analysis efforts. • Tasks 8 and 11: Scoping fire modeling and detailed fire modeling. More focus should be placed on detailed fire modeling and less focus on scoping fire modeling. This was the approach taken for the fire PRA. • Task 14: Fire risk quantification. Typically, multiple safe shutdown (SSD) components fail during a given fire scenario. Therefore dependent failure analysis is critical to obtaining a meaningful fire risk quantification. Dependent failure analysis for the fire PRA presented several challenges which will be discussed in the full paper.« less

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.

Risk analysis of urban gas pipeline network based on improved bow-tie model

NASA Astrophysics Data System (ADS)

Hao, M. J.; You, Q. J.; Yue, Z.

2017-11-01

Gas pipeline network is a major hazard source in urban areas. In the event of an accident, there could be grave consequences. In order to understand more clearly the causes and consequences of gas pipeline network accidents, and to develop prevention and mitigation measures, the author puts forward the application of improved bow-tie model to analyze risks of urban gas pipeline network. The improved bow-tie model analyzes accident causes from four aspects: human, materials, environment and management; it also analyzes the consequences from four aspects: casualty, property loss, environment and society. Then it quantifies the causes and consequences. Risk identification, risk analysis, risk assessment, risk control, and risk management will be clearly shown in the model figures. Then it can suggest prevention and mitigation measures accordingly to help reduce accident rate of gas pipeline network. The results show that the whole process of an accident can be visually investigated using the bow-tie model. It can also provide reasons for and predict consequences of an unfortunate event. It is of great significance in order to analyze leakage failure of gas pipeline network.

Claims-based risk model for first severe COPD exacerbation.

PubMed

Stanford, Richard H; Nag, Arpita; Mapel, Douglas W; Lee, Todd A; Rosiello, Richard; Schatz, Michael; Vekeman, Francis; Gauthier-Loiselle, Marjolaine; Merrigan, J F Philip; Duh, Mei Sheng

2018-02-01

To develop and validate a predictive model for first severe chronic obstructive pulmonary disease (COPD) exacerbation using health insurance claims data and to validate the risk measure of controller medication to total COPD treatment (controller and rescue) ratio (CTR). A predictive model was developed and validated in 2 managed care databases: Truven Health MarketScan database and Reliant Medical Group database. This secondary analysis assessed risk factors, including CTR, during the baseline period (Year 1) to predict risk of severe exacerbation in the at-risk period (Year 2). Patients with COPD who were 40 years or older and who had at least 1 COPD medication dispensed during the year following COPD diagnosis were included. Subjects with severe exacerbations in the baseline year were excluded. Risk factors in the baseline period were included as potential predictors in multivariate analysis. Performance was evaluated using C-statistics. The analysis included 223,824 patients. The greatest risk factors for first severe exacerbation were advanced age, chronic oxygen therapy usage, COPD diagnosis type, dispensing of 4 or more canisters of rescue medication, and having 2 or more moderate exacerbations. A CTR of 0.3 or greater was associated with a 14% lower risk of severe exacerbation. The model performed well with C-statistics, ranging from 0.711 to 0.714. This claims-based risk model can predict the likelihood of first severe COPD exacerbation. The CTR could also potentially be used to target populations at greatest risk for severe exacerbations. This could be relevant for providers and payers in approaches to prevent severe exacerbations and reduce costs.

Comparative and Predictive Multimedia Assessments Using Monte Carlo Uncertainty Analyses

NASA Astrophysics Data System (ADS)

Whelan, G.

2002-05-01

Multiple-pathway frameworks (sometimes referred to as multimedia models) provide a platform for combining medium-specific environmental models and databases, such that they can be utilized in a more holistic assessment of contaminant fate and transport in the environment. These frameworks provide a relatively seamless transfer of information from one model to the next and from databases to models. Within these frameworks, multiple models are linked, resulting in models that consume information from upstream models and produce information to be consumed by downstream models. The Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES) is an example, which allows users to link their models to other models and databases. FRAMES is an icon-driven, site-layout platform that is an open-architecture, object-oriented system that interacts with environmental databases; helps the user construct a Conceptual Site Model that is real-world based; allows the user to choose the most appropriate models to solve simulation requirements; solves the standard risk paradigm of release transport and fate; and exposure/risk assessments to people and ecology; and presents graphical packages for analyzing results. FRAMES is specifically designed allow users to link their own models into a system, which contains models developed by others. This paper will present the use of FRAMES to evaluate potential human health exposures using real site data and realistic assumptions from sources, through the vadose and saturated zones, to exposure and risk assessment at three real-world sites, using the Multimedia Environmental Pollutant Assessment System (MEPAS), which is a multimedia model contained within FRAMES. These real-world examples use predictive and comparative approaches coupled with a Monte Carlo analysis. A predictive analysis is where models are calibrated to monitored site data, prior to the assessment, and a comparative analysis is where models are not calibrated but based solely on literature or judgement and is usually used to compare alternatives. In many cases, a combination is employed where the model is calibrated to a portion of the data (e.g., to determine hydrodynamics), then used to compare alternatives. Three subsurface-based multimedia examples are presented, increasing in complexity. The first presents the application of a predictive, deterministic assessment; the second presents a predictive and comparative, Monte Carlo analysis; and the third presents a comparative, multi-dimensional Monte Carlo analysis. Endpoints are typically presented in terms of concentration, hazard, risk, and dose, and because the vadose zone model typically represents a connection between a source and the aquifer, it does not generally represent the final medium in a multimedia risk assessment.

Fault tree analysis for integrated and probabilistic risk analysis of drinking water systems.

PubMed

Lindhe, Andreas; Rosén, Lars; Norberg, Tommy; Bergstedt, Olof

2009-04-01

Drinking water systems are vulnerable and subject to a wide range of risks. To avoid sub-optimisation of risk-reduction options, risk analyses need to include the entire drinking water system, from source to tap. Such an integrated approach demands tools that are able to model interactions between different events. Fault tree analysis is a risk estimation tool with the ability to model interactions between events. Using fault tree analysis on an integrated level, a probabilistic risk analysis of a large drinking water system in Sweden was carried out. The primary aims of the study were: (1) to develop a method for integrated and probabilistic risk analysis of entire drinking water systems; and (2) to evaluate the applicability of Customer Minutes Lost (CML) as a measure of risk. The analysis included situations where no water is delivered to the consumer (quantity failure) and situations where water is delivered but does not comply with water quality standards (quality failure). Hard data as well as expert judgements were used to estimate probabilities of events and uncertainties in the estimates. The calculations were performed using Monte Carlo simulations. CML is shown to be a useful measure of risks associated with drinking water systems. The method presented provides information on risk levels, probabilities of failure, failure rates and downtimes of the system. This information is available for the entire system as well as its different sub-systems. Furthermore, the method enables comparison of the results with performance targets and acceptable levels of risk. The method thus facilitates integrated risk analysis and consequently helps decision-makers to minimise sub-optimisation of risk-reduction options.

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 review and critique of some models used in competing risk analysis.

PubMed

Gail, M

1975-03-01

We have introduced a notation which allows one to define competing risk models easily and to examine underlying assumptions. We have treated the actuarial model for competing risk in detail, comparing it with other models and giving useful variance formulae both for the case when times of death are available and for the case when they are not. The generality of these methods is illustrated by an example treating two dependent competing risks.

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.

Association between the MTHFR A1298C polymorphism and risk of cancer: evidence from 265 case-control studies.

PubMed

Zhu, Xin-Li; Liu, Zhi-Zhong; Yan, Sen-Xiang; Wang, Wei; Chang, Rui-Xia; Zhang, Chun-Yan; Guo, Yan

2016-02-01

Many molecular, epidemiological studies have been performed to explore the association between MTHFR A1298C polymorphism and cancer risk. However, the results were inconsistent or even contradictory. Hence, we performed a meta-analysis to investigate the association between cancer risk and MTHFR A1298C (81,040 cases and 114,975 controls from 265 studies) polymorphism. Overall, significant association was observed between MTHFR A1298C polymorphism and cancer risk when all eligible studies were pooled into the meta-analysis. In further stratified and sensitivity analyses, significantly increased cervical cancer (dominant model: OR 1.46, 95 % CI 1.13-1.90; AC vs. AA: OR 1.48, 95 % CI 1.13-1.92) and lymphoma (dominant model: OR 1.22, 95 % CI 1.04-1.44; recessive model: OR 1.66, 95 % CI 1.15-2.39; CC vs. AA: OR 1.75, 95 % CI 1.21-2.53) risk were observed in Asians, and significantly decreased colorectal cancer risk was found in Asians (recessive model: OR 0.75, 95 % CI 0.59-0.96; CC vs. AA: OR 0.77, 95 % CI 0.60-1.00). In summary, this meta-analysis suggests that MTHFR A1298C polymorphism is associated with increased cervical cancer and lymphoma risk in Asians, and MTHFR A1298C polymorphism is associated with decreased colorectal cancer risk in Asians. Moreover, this meta-analysis also points out the importance of new studies, such as oral cancer and chronic myeloid leukemia, because they had high heterogeneity in this meta-analysis (I (2) > 75 %).

A Risk-Analysis Approach to Implementing Web-Based Assessment

ERIC Educational Resources Information Center

Ricketts, Chris; Zakrzewski, Stan

2005-01-01

Computer-Based Assessment is a risky business. This paper proposes the use of a model for web-based assessment systems that identifies pedagogic, operational, technical (non web-based), web-based and financial risks. The strategies and procedures for risk elimination or reduction arise from risk analysis and management and are the means by which…

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

PubMed

Xin, Cao; Chongshi, Gu

2016-01-01

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

Bactericidal/permeability increasing protein gene polymorphism and inflammatory bowel diseases: meta-analysis of five case-control studies.

PubMed

Fan, Lijuan; Fu, Guoning; Ding, Yuanyuan; Lv, Peng; Li, Hongyun

2017-03-01

Bactericidal/permeability increasing protein (BPI) gene polymorphisms have been extensively investigated in terms of their associations with inflammatory bowel disease (IBD), with contradictory results. The aim of this meta-analysis was to evaluate associations between BPI gene polymorphisms and the risk of IBD, Crohn's disease (CD), and ulcerative colitis (UC). Eligible studies from PubMed, Embase, and Cochrane library databases were identified. Ten studies (five CD and five UC) published in five papers were included in this meta-analysis. G645A polymorphism was associated with a decreased risk of UC in allele model, dominant model, and homozygous model. Our data suggested that BPI G645A polymorphism was associated with a decreased risk of UC; the BPI G645A polymorphism was not associated with the risk of CD.

A GIS-based approach for comparative analysis of potential fire risk assessment

NASA Astrophysics Data System (ADS)

Sun, Ying; Hu, Lieqiu; Liu, Huiping

2007-06-01

Urban fires are one of the most important sources of property loss and human casualty and therefore it is necessary to assess the potential fire risk with consideration of urban community safety. Two evaluation models are proposed, both of which are integrated with GIS. One is the single factor model concerning the accessibility of fire passage and the other is grey clustering approach based on the multifactor system. In the latter model, fourteen factors are introduced and divided into four categories involving security management, evacuation facility, construction resistance and fire fighting capability. A case study on campus of Beijing Normal University is presented to express the potential risk assessment models in details. A comparative analysis of the two models is carried out to validate the accuracy. The results are approximately consistent with each other. Moreover, modeling with GIS promotes the efficiency the potential risk assessment.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

The analysis of factors of management of safety of critical information infrastructure with use of dynamic models

NASA Astrophysics Data System (ADS)

Trostyansky, S. N.; Kalach, A. V.; Lavlinsky, V. V.; Lankin, O. V.

2018-03-01

Based on the analysis of the dynamic model of panel data by region, including fire statistics for surveillance sites and statistics of a set of regional socio-economic indicators, as well as the time of rapid response of the state fire service to fires, the probability of fires in the surveillance sites and the risk of human death in The result of such fires from the values of the corresponding indicators for the previous year, a set of regional social-economics factors, as well as regional indicators time rapid response of the state fire service in the fire. The results obtained are consistent with the results of the application to the fire risks of the model of a rational offender. Estimation of the economic equivalent of human life from data on surveillance objects for Russia, calculated on the basis of the analysis of the presented dynamic model of fire risks, correctly agrees with the known literary data. The results obtained on the basis of the econometric approach to fire risks allow us to forecast fire risks at the supervisory sites in the regions of Russia and to develop management solutions to minimize such risks.

Malaria Disease Mapping in Malaysia based on Besag-York-Mollie (BYM) Model

NASA Astrophysics Data System (ADS)

Azah Samat, Nor; Mey, Liew Wan

2017-09-01

Disease mapping is the visual representation of the geographical distribution which give an overview info about the incidence of disease within a population through spatial epidemiology data. Based on the result of map, it helps in monitoring and planning resource needs at all levels of health care and designing appropriate interventions, tailored towards areas that deserve closer scrutiny or communities that lead to further investigations to identify important risk factors. Therefore, the choice of statistical model used for relative risk estimation is important because production of disease risk map relies on the model used. This paper proposes Besag-York-Mollie (BYM) model to estimate the relative risk for Malaria in Malaysia. The analysis involved using the number of Malaria cases that obtained from the Ministry of Health Malaysia. The outcomes of analysis are displayed through graph and map, including Malaria disease risk map that constructed according to the estimation of relative risk. The distribution of high and low risk areas of Malaria disease occurrences for all states in Malaysia can be identified in the risk map.

Bayesian-network-based safety risk assessment for steel construction projects.

PubMed

Leu, Sou-Sen; Chang, Ching-Miao

2013-05-01

There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites. Copyright © 2013 Elsevier Ltd. All rights reserved.

TNF-308 G/A polymorphism and risk of acne vulgaris: a meta-analysis.

PubMed

Yang, Jian-Kang; Wu, Wen-Juan; Qi, Jue; He, Li; Zhang, Ya-Ping

2014-01-01

The -308 G/A polymorphism in the tumor necrosis factor (TNF) gene has been implicated in the risk of acne vulgaris, but the results are inconclusive. The present meta-analysis aimed to investigate the overall association between the -308 G/A polymorphism and acne vulgaris risk. We searched in Pubmed, Embase, Web of Science and CNKI for studies evaluating the association between the -308 G/A gene polymorphism and acne vulgaris risk. Data were extracted and statistical analysis was performed using STATA 12.0 software. A total of five publications involving 1553 subjects (728 acne vulgaris cases and 825 controls) were included in this meta-analysis. Combined analysis revealed a significant association between this polymorphism and acne vulgaris risk under recessive model (OR = 2.73, 95% CI: 1.37-5.44, p = 0.004 for AA vs. AG + GG). Subgroup analysis by ethnicity showed that the acne vulgaris risk associated with the -308 G/A gene polymorphism was significantly elevated among Caucasians under recessive model (OR = 2.34, 95% CI: 1.13-4.86, p = 0.023). This meta-analysis suggests that the -308 G/A polymorphism in the TNF gene contributes to acne vulgaris risk, especially in Caucasian populations. Further studies among different ethnicity populations are needed to validate these findings.

Adversarial Risk Analysis for Urban Security Resource Allocation.

PubMed

Gil, César; Rios Insua, David; Rios, Jesus

2016-04-01

Adversarial risk analysis (ARA) provides a framework to deal with risks originating from intentional actions of adversaries. We show how ARA may be used to allocate security resources in the protection of urban spaces. We take into account the spatial structure and consider both proactive and reactive measures, in that we aim at both trying to reduce criminality as well as recovering as best as possible from it, should it happen. We deal with the problem by deploying an ARA model over each spatial unit, coordinating the models through resource constraints, value aggregation, and proximity. We illustrate our approach with an example that uncovers several relevant policy issues. © 2016 Society for Risk Analysis.

Comparative analysis of bleeding risk by the location and shape of arachnoid cysts: a finite element model analysis.

PubMed

Lee, Chang-Hyun; Han, In Seok; Lee, Ji Yeoun; Phi, Ji Hoon; Kim, Seung-Ki; Kim, Young-Eun; Wang, Kyu-Chang

2017-01-01

Although arachnoid cysts (ACs) are observed in various locations, only sylvian ACs are mainly regarded to be associated with bleeding. The reason for this selective association of sylvian ACs with bleeding is not understood well. This study is to investigate the effect of the location and shape of ACs on the risk of bleeding. A developed finite element model of the head/brain was modified for models of sylvian, suprasellar, and posterior fossa ACs. A spherical AC was placed at each location to compare the effect of AC location. Bowl-shaped and oval-shaped AC models were developed to compare the effect by shape. The shear force on the spot-weld elements (SFSW) was measured between the dura and the outer wall of the ACs or the comparable arachnoid membrane in the normal model. All AC models revealed higher SFSW than comparable normal models. By location, sylvian AC displayed the highest SFSW for frontal and lateral impacts. By shape, small outer wall AC models showed higher SFSW than large wall models in sylvian area and lower SFSW than large ones in posterior fossa. In regression analysis, the presence of AC was the only independent risk of bleeding. The bleeding mechanism of ACs is very complex, and the risk quantification failed to show a significant role of location and shape of ACs. The presence of AC increases shear force on impact condition and may be a risk factor of bleeding, and sylvian location of AC may not have additive risks of AC bleeding.

TNFalpha -308 G/A polymorphism is associated with breast cancer risk: a meta-analysis involving 10,184 cases and 12,911 controls.

PubMed

Fang, Fang; Yao, Lei; Yu, Xiao Jia; Yu, Lu; Wu, Qi; Yu, Long

2010-07-01

Tumor necrosis factor alpha (TNFalpha) is a pleiotropic cytokine which can regulate a wide variety of cellular responses. Low concentrations of TNFalpha seem to increase tumor growth and progression. The -308 G/A polymorphism in TNFalpha has been implicated in breast cancer risk but the published data remain inconclusive. In order to derive a more precise estimation of the relationship, a meta-analysis was performed by searching PubMed, Web of Science, ScienceDirect, EBSCO, CNKI, and Chinese Biomedicine Database. 11 studies including 10,184 cases and 12,911 controls were collected for TNFalpha -308 G/A polymorphism. Crude ORs with 95% CIs were used to assess the strength of association between the TNFalpha -308 G/A polymorphism and breast cancer risk. The pooled ORs were performed for codominant model (GG versus AA; GA versus AA), dominant model (GG + GA versus AA), recessive model (GG versus GA + AA), and G allele versus A allele, respectively. Overall, significantly elevated breast cancer risk was found for recessive model (OR = 1.10, 95% CI = 1.04-1.17) and for G allele versus A allele (OR = 1.08, 95% CI = 1.02-1.14). In the subgroup analysis by ethnicity, significantly increased risks were also found among Caucasians for recessive model and for G allele versus A allele (for recessive model: OR = 1.10, 95% CI = 1.04-1.17; for G allele versus A allele: OR = 1.09, 95% CI = 1.03-1.14). However, no significant associations were found among Asians for all genetic models. In conclusion, this meta-analysis suggests that the TNFalpha -308 G allele is a risk factor for developing breast cancer, especially for Caucasians.

The Abdominal Aortic Aneurysm Statistically Corrected Operative Risk Evaluation (AAA SCORE) for predicting mortality after open and endovascular interventions.

PubMed

Ambler, Graeme K; Gohel, Manjit S; Mitchell, David C; Loftus, Ian M; Boyle, Jonathan R

2015-01-01

Accurate adjustment of surgical outcome data for risk is vital in an era of surgeon-level reporting. Current risk prediction models for abdominal aortic aneurysm (AAA) repair are suboptimal. We aimed to develop a reliable risk model for in-hospital mortality after intervention for AAA, using rigorous contemporary statistical techniques to handle missing data. Using data collected during a 15-month period in the United Kingdom National Vascular Database, we applied multiple imputation methodology together with stepwise model selection to generate preoperative and perioperative models of in-hospital mortality after AAA repair, using two thirds of the available data. Model performance was then assessed on the remaining third of the data by receiver operating characteristic curve analysis and compared with existing risk prediction models. Model calibration was assessed by Hosmer-Lemeshow analysis. A total of 8088 AAA repair operations were recorded in the National Vascular Database during the study period, of which 5870 (72.6%) were elective procedures. Both preoperative and perioperative models showed excellent discrimination, with areas under the receiver operating characteristic curve of .89 and .92, respectively. This was significantly better than any of the existing models (area under the receiver operating characteristic curve for best comparator model, .84 and .88; P < .001 and P = .001, respectively). Discrimination remained excellent when only elective procedures were considered. There was no evidence of miscalibration by Hosmer-Lemeshow analysis. We have developed accurate models to assess risk of in-hospital mortality after AAA repair. These models were carefully developed with rigorous statistical methodology and significantly outperform existing methods for both elective cases and overall AAA mortality. These models will be invaluable for both preoperative patient counseling and accurate risk adjustment of published outcome data. Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

An Emerging New Risk Analysis Science: Foundations and Implications.

PubMed

Aven, Terje

2018-05-01

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

Credibility analysis of risk classes by generalized linear model

NASA Astrophysics Data System (ADS)

Erdemir, Ovgucan Karadag; Sucu, Meral

2016-06-01

In this paper generalized linear model (GLM) and credibility theory which are frequently used in nonlife insurance pricing are combined for reliability analysis. Using full credibility standard, GLM is associated with limited fluctuation credibility approach. Comparison criteria such as asymptotic variance and credibility probability are used to analyze the credibility of risk classes. An application is performed by using one-year claim frequency data of a Turkish insurance company and results of credible risk classes are interpreted.

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.

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.

A quality risk management model approach for cell therapy manufacturing.

PubMed

Lopez, Fabio; Di Bartolo, Chiara; Piazza, Tommaso; Passannanti, Antonino; Gerlach, Jörg C; Gridelli, Bruno; Triolo, Fabio

2010-12-01

International regulatory authorities view risk management as an essential production need for the development of innovative, somatic cell-based therapies in regenerative medicine. The available risk management guidelines, however, provide little guidance on specific risk analysis approaches and procedures applicable in clinical cell therapy manufacturing. This raises a number of problems. Cell manufacturing is a poorly automated process, prone to operator-introduced variations, and affected by heterogeneity of the processed organs/tissues and lot-dependent variability of reagent (e.g., collagenase) efficiency. In this study, the principal challenges faced in a cell-based product manufacturing context (i.e., high dependence on human intervention and absence of reference standards for acceptable risk levels) are identified and addressed, and a risk management model approach applicable to manufacturing of cells for clinical use is described for the first time. The use of the heuristic and pseudo-quantitative failure mode and effect analysis/failure mode and critical effect analysis risk analysis technique associated with direct estimation of severity, occurrence, and detection is, in this specific context, as effective as, but more efficient than, the analytic hierarchy process. Moreover, a severity/occurrence matrix and Pareto analysis can be successfully adopted to identify priority failure modes on which to act to mitigate risks. The application of this approach to clinical cell therapy manufacturing in regenerative medicine is also discussed. © 2010 Society for Risk Analysis.

Using decision tree analysis to identify risk factors for relapse to smoking

PubMed Central

Piper, Megan E.; Loh, Wei-Yin; Smith, Stevens S.; Japuntich, Sandra J.; Baker, Timothy B.

2010-01-01

This research used classification tree analysis and logistic regression models to identify risk factors related to short- and long-term abstinence. Baseline and cessation outcome data from two smoking cessation trials, conducted from 2001 to 2002, in two Midwestern urban areas, were analyzed. There were 928 participants (53.1% women, 81.8% white) with complete data. Both analyses suggest that relapse risk is produced by interactions of risk factors and that early and late cessation outcomes reflect different vulnerability factors. The results illustrate the dynamic nature of relapse risk and suggest the importance of efficient modeling of interactions in relapse prediction. PMID:20397871

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...

Individual risk factors for deep infection and compromised fracture healing after intramedullary nailing of tibial shaft fractures: a single centre experience of 480 patients.

PubMed

Metsemakers, W-J; Handojo, K; Reynders, P; Sermon, A; Vanderschot, P; Nijs, S

2015-04-01

Despite modern advances in the treatment of tibial shaft fractures, complications including nonunion, malunion, and infection remain relatively frequent. A better understanding of these injuries and its complications could lead to prevention rather than treatment strategies. A retrospective study was performed to identify risk factors for deep infection and compromised fracture healing after intramedullary nailing (IMN) of tibial shaft fractures. Between January 2000 and January 2012, 480 consecutive patients with 486 tibial shaft fractures were enrolled in the study. Statistical analysis was performed to determine predictors of deep infection and compromised fracture healing. Compromised fracture healing was subdivided in delayed union and nonunion. The following independent variables were selected for analysis: age, sex, smoking, obesity, diabetes, American Society of Anaesthesiologists (ASA) classification, polytrauma, fracture type, open fractures, Gustilo type, primary external fixation (EF), time to nailing (TTN) and reaming. As primary statistical evaluation we performed a univariate analysis, followed by a multiple logistic regression model. Univariate regression analysis revealed similar risk factors for delayed union and nonunion, including fracture type, open fractures and Gustilo type. Factors affecting the occurrence of deep infection in this model were primary EF, a prolonged TTN, open fractures and Gustilo type. Multiple logistic regression analysis revealed polytrauma as the single risk factor for nonunion. With respect to delayed union, no risk factors could be identified. In the same statistical model, deep infection was correlated with primary EF. The purpose of this study was to evaluate risk factors of poor outcome after IMN of tibial shaft fractures. The univariate regression analysis showed that the nature of complications after tibial shaft nailing could be multifactorial. This was not confirmed in a multiple logistic regression model, which only revealed polytrauma and primary EF as risk factors for nonunion and deep infection, respectively. Future strategies should focus on prevention in high-risk populations such as polytrauma patients treated with EF. Copyright © 2014 Elsevier Ltd. All rights reserved.

Validation of a new mortality risk prediction model for people 65 years and older in northwest Russia: The Crystal risk score.

PubMed

Turusheva, Anna; Frolova, Elena; Bert, Vaes; Hegendoerfer, Eralda; Degryse, Jean-Marie

2017-07-01

Prediction models help to make decisions about further management in clinical practice. This study aims to develop a mortality risk score based on previously identified risk predictors and to perform internal and external validations. In a population-based prospective cohort study of 611 community-dwelling individuals aged 65+ in St. Petersburg (Russia), all-cause mortality risks over 2.5 years follow-up were determined based on the results obtained from anthropometry, medical history, physical performance tests, spirometry and laboratory tests. C-statistic, risk reclassification analysis, integrated discrimination improvement analysis, decision curves analysis, internal validation and external validation were performed. Older adults were at higher risk for mortality [HR (95%CI)=4.54 (3.73-5.52)] when two or more of the following components were present: poor physical performance, low muscle mass, poor lung function, and anemia. If anemia was combined with high C-reactive protein (CRP) and high B-type natriuretic peptide (BNP) was added the HR (95%CI) was slightly higher (5.81 (4.73-7.14)) even after adjusting for age, sex and comorbidities. Our models were validated in an external population of adults 80+. The extended model had a better predictive capacity for cardiovascular mortality [HR (95%CI)=5.05 (2.23-11.44)] compared to the baseline model [HR (95%CI)=2.17 (1.18-4.00)] in the external population. We developed and validated a new risk prediction score that may be used to identify older adults at higher risk for mortality in Russia. Additional studies need to determine which targeted interventions improve the outcomes of these at-risk individuals. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting surgical site infection after spine surgery: a validated model using a prospective surgical registry.

PubMed

Lee, Michael J; Cizik, Amy M; Hamilton, Deven; Chapman, Jens R

2014-09-01

The impact of surgical site infection (SSI) is substantial. Although previous study has determined relative risk and odds ratio (OR) values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of SSI, rather than relative risk or OR values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. The purpose of this study was to create and validate a predictive model for the risk of SSI after spine surgery. This study performs a multivariate analysis of SSI after spine surgery using a large prospective surgical registry. Using the results of this analysis, this study will then create and validate a predictive model for SSI after spine surgery. The patient sample is from a high-quality surgical registry from our two institutions with prospectively collected, detailed demographic, comorbidity, and complication data. An SSI that required return to the operating room for surgical debridement. Using a prospectively collected surgical registry of more than 1,532 patients with extensive demographic, comorbidity, surgical, and complication details recorded for 2 years after the surgery, we identified several risk factors for SSI after multivariate analysis. Using the beta coefficients from those regression analyses, we created a model to predict the occurrence of SSI after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created a predictive model based on our beta coefficients from our multivariate analysis. The final predictive model for SSI had a receiver-operator curve characteristic of 0.72, considered to be a fair measure. The final model has been uploaded for use on SpineSage.com. We present a validated model for predicting SSI after spine surgery. The value in this model is that it gives the user an absolute percent likelihood of SSI after spine surgery based on the patient's comorbidity profile and invasiveness of surgery. Patients are far more likely to understand an absolute percentage, rather than relative risk and confidence interval values. A model such as this is of paramount importance in counseling patients and enhancing the safety of spine surgery. In addition, a tool such as this can be of great use particularly as health care trends toward pay for performance, quality metrics (such as SSI), and risk adjustment. To facilitate the use of this model, we have created a Web site (SpineSage.com) where users can enter patient data to determine likelihood for SSI. Copyright © 2014 Elsevier Inc. All rights reserved.

A genetic variant in MiR-146a modifies digestive system cancer risk: a meta-analysis.

PubMed

Li, Ying-Jun; Zhang, Zhen-Yu; Mao, Ying-Ying; Jin, Ming-Juan; Jing, Fang-Yuan; Ye, Zhen-Hua; Chen, Kun

2014-01-01

MicroRNAs (miRNAs) negatively regulate gene expression and act as tumor suppressors or oncogenes in oncogenesis. The association between a single nucleotide polymorphism (SNP) in miR-146a rs2910164 and susceptibility to digestive system cancers was inconsistent in previous studies. In this study, we conducted a literature search of PubMed to identify all relevant studies published before August 31, 2013. A total of 21 independent case-control studies were included in this updated meta-analysis with 9,558 cases and 10,614 controls. We found that the miR-146a rs2910164 polymorphism was significantly associated with decreased risk of digestive system cancers in an allele model (OR=0.90, 95%CI 0.87-0.94), homozygote model (OR=0.84, 95%CI 0.77-0.91), dominant model (OR=0.90, 95%CI 0.84-0.96), and recessive model (OR=0.85, 95%CI 0.79-0.91), while in a heterozygous model (OR = 0.99, 95% CI 0.89-1.11) the association showed marginal significance. Subgroup analysis by cancer site revealed decreased risk in colorectal cancer above allele model (OR=0.90, 95%CI 0.83- 0.97) and homozygote model (OR=0.85, 95%CI 0.72-1.00). Similarly, decreased cancer risk was observed when compared with allele model (OR=0.87, 95%CI 0.81-0.93) and recessive model (OR=0.81, 95%CI 0.72-0.90) in gastric cancer. When stratified by ethnicity, genotyping methods and quality score, decreased cancer risks were also observed. This current meta-analysis indicated that miR-146a rs2910164 polymorphism may decrease the susceptibility to digestive system cancers, especially in Asian populations.

A discriminant analysis prediction model of non-syndromic cleft lip with or without cleft palate based on risk factors.

PubMed

Li, Huixia; Luo, Miyang; Luo, Jiayou; Zheng, Jianfei; Zeng, Rong; Du, Qiyun; Fang, Junqun; Ouyang, Na

2016-11-23

A risk prediction model of non-syndromic cleft lip with or without cleft palate (NSCL/P) was established by a discriminant analysis to predict the individual risk of NSCL/P in pregnant women. A hospital-based case-control study was conducted with 113 cases of NSCL/P and 226 controls without NSCL/P. The cases and the controls were obtained from 52 birth defects' surveillance hospitals in Hunan Province, China. A questionnaire was administered in person to collect the variables relevant to NSCL/P by face to face interviews. Logistic regression models were used to analyze the influencing factors of NSCL/P, and a stepwise Fisher discriminant analysis was subsequently used to construct the prediction model. In the univariate analysis, 13 influencing factors were related to NSCL/P, of which the following 8 influencing factors as predictors determined the discriminant prediction model: family income, maternal occupational hazards exposure, premarital medical examination, housing renovation, milk/soymilk intake in the first trimester of pregnancy, paternal occupational hazards exposure, paternal strong tea drinking, and family history of NSCL/P. The model had statistical significance (lambda = 0.772, chi-square = 86.044, df = 8, P < 0.001). Self-verification showed that 83.8 % of the participants were correctly predicted to be NSCL/P cases or controls with a sensitivity of 74.3 % and a specificity of 88.5 %. The area under the receiver operating characteristic curve (AUC) was 0.846. The prediction model that was established using the risk factors of NSCL/P can be useful for predicting the risk of NSCL/P. Further research is needed to improve the model, and confirm the validity and reliability of the model.

The risk typology of healthcare access and its association with unmet healthcare needs in Asian Americans.

PubMed

Jang, Yuri; Park, Nan Sook; Yoon, Hyunwoo; Huang, Ya-Ching; Rhee, Min-Kyoung; Chiriboga, David A; Kim, Miyong T

2018-01-01

Using data from the 2015 Asian American Quality of Life Survey (N = 2,609), latent profile analysis was conducted on general (health insurance, usual place for care and income) and immigrant-specific (nativity, length of stay in the U.S., English proficiency and acculturation) risk factors of healthcare access. Latent profile analysis identified a three-cluster model (low-risk, moderate-risk and high-risk groups). Compared with the low-risk group, the odds of having an unmet healthcare need was 1.52 times greater in the moderate-risk group and 2.24 times greater in the high-risk group. Challenging the myth of model minority, the present sample of Asian Americans demonstrates its vulnerability in access to healthcare. Findings also show the heterogeneity in healthcare access risk profiles. © 2017 John Wiley & Sons Ltd.

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

PubMed

Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

2015-07-01

As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

PredictABEL: an R package for the assessment of risk prediction models.

PubMed

Kundu, Suman; Aulchenko, Yurii S; van Duijn, Cornelia M; Janssens, A Cecile J W

2011-04-01

The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL ( http://www.genabel.org ) and CRAN ( http://cran.r-project.org/).

An Accident Precursor Analysis Process Tailored for NASA Space Systems

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

Comparative assessment of analytical approaches to quantify the risk for introduction of rare animal diseases: the example of avian influenza in Spain.

PubMed

Sánchez-Vizcaíno, Fernando; Perez, Andrés; Martínez-López, Beatriz; Sánchez-Vizcaíno, José Manuel

2012-08-01

Trade of animals and animal products imposes an uncertain and variable risk for exotic animal diseases introduction into importing countries. Risk analysis provides importing countries with an objective, transparent, and internationally accepted method for assessing that risk. Over the last decades, European Union countries have conducted probabilistic risk assessments quite frequently to quantify the risk for rare animal diseases introduction into their territories. Most probabilistic animal health risk assessments have been typically classified into one-level and multilevel binomial models. One-level models are more simple than multilevel models because they assume that animals or products originate from one single population. However, it is unknown whether such simplification may result in substantially different results compared to those obtained through the use of multilevel models. Here, data used on a probabilistic multilevel binomial model formulated to assess the risk for highly pathogenic avian influenza introduction into Spain were reanalyzed using a one-level binomial model and their outcomes were compared. An alternative ordinal model is also proposed here, which makes use of simpler assumptions and less information compared to those required by traditional one-level and multilevel approaches. Results suggest that, at least under certain circumstances, results of the one-level and ordinal approaches are similar to those obtained using multilevel models. Consequently, we argue that, when data are insufficient to run traditional probabilistic models, the ordinal approach presented here may be a suitable alternative to rank exporting countries in terms of the risk that they impose for the spread of rare animal diseases into disease-free countries. © 2012 Society for Risk Analysis.

A method for scenario-based risk assessment for robust aerospace systems

NASA Astrophysics Data System (ADS)

Thomas, Victoria Katherine

In years past, aircraft conceptual design centered around creating a feasible aircraft that could be built and could fly the required missions. More recently, aircraft viability entered into conceptual design, allowing that the product's potential to be profitable should also be examined early in the design process. While examining an aerospace system's feasibility and viability early in the design process is extremely important, it is also important to examine system risk. In traditional aerospace systems risk analysis, risk is examined from the perspective of performance, schedule, and cost. Recently, safety and reliability analysis have been brought forward in the design process to also be examined during late conceptual and early preliminary design. While these analyses work as designed, existing risk analysis methods and techniques are not designed to examine an aerospace system's external operating environment and the risks present there. A new method has been developed here to examine, during the early part of concept design, the risk associated with not meeting assumptions about the system's external operating environment. The risks are examined in five categories: employment, culture, government and politics, economics, and technology. The risks are examined over a long time-period, up to the system's entire life cycle. The method consists of eight steps over three focus areas. The first focus area is Problem Setup. During problem setup, the problem is defined and understood to the best of the decision maker's ability. There are four steps in this area, in the following order: Establish the Need, Scenario Development, Identify Solution Alternatives, and Uncertainty and Risk Identification. There is significant iteration between steps two through four. Focus area two is Modeling and Simulation. In this area the solution alternatives and risks are modeled, and a numerical value for risk is calculated. A risk mitigation model is also created. The four steps involved in completing the modeling and simulation are: Alternative Solution Modeling, Uncertainty Quantification, Risk Assessment, and Risk Mitigation. Focus area three consists of Decision Support. In this area a decision support interface is created that allows for game playing between solution alternatives and risk mitigation. A multi-attribute decision making process is also implemented to aid in decision making. A demonstration problem inspired by Airbus' mid 1980s decision to break into the widebody long-range market was developed to illustrate the use of this method. The results showed that the method is able to capture additional types of risk than previous analysis methods, particularly at the early stages of aircraft design. It was also shown that the method can be used to help create a system that is robust to external environmental factors. The addition of an external environment risk analysis in the early stages of conceptual design can add another dimension to the analysis of feasibility and viability. The ability to take risk into account during the early stages of the design process can allow for the elimination of potentially feasible and viable but too-risky alternatives. The addition of a scenario-based analysis instead of a traditional probabilistic analysis enabled uncertainty to be effectively bound and examined over a variety of potential futures instead of only a single future. There is also potential for a product to be groomed for a specific future that one believes is likely to happen, or for a product to be steered during design as the future unfolds.

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

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

Lepinski, James

2013-09-30

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

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.

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

PubMed

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

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Atlas-guided volumetric diffuse optical tomography enhanced by generalized linear model analysis to image risk decision-making responses in young adults.

PubMed

Lin, Zi-Jing; Li, Lin; Cazzell, Mary; Liu, Hanli

2014-08-01

Diffuse optical tomography (DOT) is a variant of functional near infrared spectroscopy and has the capability of mapping or reconstructing three dimensional (3D) hemodynamic changes due to brain activity. Common methods used in DOT image analysis to define brain activation have limitations because the selection of activation period is relatively subjective. General linear model (GLM)-based analysis can overcome this limitation. In this study, we combine the atlas-guided 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with risk decision-making processes. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The Balloon Analog Risk Task (BART) is a valid experimental model and has been commonly used to assess human risk-taking actions and tendencies while facing risks. We have used the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making from 37 human participants (22 males and 15 females). Voxel-wise GLM analysis was performed after a human brain atlas template and a depth compensation algorithm were combined to form atlas-guided DOT images. In this work, we wish to demonstrate the excellence of using voxel-wise GLM analysis with DOT to image and study cognitive functions in response to risk decision-making. Results have shown significant hemodynamic changes in the dorsal lateral prefrontal cortex (DLPFC) during the active-choice mode and a different activation pattern between genders; these findings correlate well with published literature in functional magnetic resonance imaging (fMRI) and fNIRS studies. Copyright © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

PubMed

Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen

2014-01-01

Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

Deficient Contractor Business Systems: Applying the Value at Risk (VAR) Model to Earned Value Management Systems

DTIC Science & Technology

2013-06-01

measuring numerical risk to the government ( Galway , 2004). However, quantitative risk analysis is rarely utilized in DoD acquisition programs because the...quantitative assessment of the EVMS itself. Galway (2004) practically linked project quantitative risk assessment to EVM by focusing on cost...Kindle version]. Retrieved from Amazon.com 83 Galway , L. (2004, February). Quantitative risk analysis for project management: A critical review

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.

Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data

PubMed Central

2014-01-01

Background Recurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurrent events within subjects, data analyses are mostly done with continuous risk interval models, which may not be appropriate for treatments with sustained effects (e.g., drug treatments of malaria patients). Furthermore, results can be biased in cases of a confounding factor implying different risk exposure, e.g. in malaria transmission: if subjects are located at zones showing different environmental factors implying different risk exposures. Methods This work aimed to compare four different approaches by analysing recurrent malaria episodes from a clinical trial assessing the effectiveness of three malaria treatments [artesunate + amodiaquine (AS + AQ), artesunate + sulphadoxine-pyrimethamine (AS + SP) or artemether-lumefantrine (AL)], with continuous and discontinuous risk intervals: Andersen-Gill counting process (AG-CP), Prentice-Williams-Peterson counting process (PWP-CP), a shared gamma frailty model, and Generalized Estimating Equations model (GEE) using Poisson distribution. Simulations were also made to analyse the impact of the addition of a confounding factor on malaria recurrent episodes. Results Using the discontinuous interval analysis, AG-CP and Shared gamma frailty models provided similar estimations of treatment effect on malaria recurrent episodes when adjusted on age category. The patients had significant decreased risk of recurrent malaria episodes when treated with AS + AQ or AS + SP arms compared to AL arm; Relative Risks were: 0.75 (95% CI (Confidence Interval): 0.62-0.89), 0.74 (95% CI: 0.62-0.88) respectively for AG-CP model and 0.76 (95% CI: 0.64-0.89), 0.74 (95% CI: 0.62-0.87) for the Shared gamma frailty model. With both discontinuous and continuous risk intervals analysis, GEE Poisson distribution models failed to detect the effect of AS + AQ arm compared to AL arm when adjusted for age category. The discontinuous risk interval analysis was found to be the more appropriate approach. Conclusion Repeated event in infectious diseases such as malaria can be analysed with appropriate existing models that account for the correlation between multiple events within subjects with common statistical software packages, after properly setting up the data structures. PMID:25073652

Markov chains and semi-Markov models in time-to-event analysis.

PubMed

Abner, Erin L; Charnigo, Richard J; Kryscio, Richard J

2013-10-25

A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields.

Markov chains and semi-Markov models in time-to-event analysis

PubMed Central

Abner, Erin L.; Charnigo, Richard J.; Kryscio, Richard J.

2014-01-01

A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields. PMID:24818062

TNF -308 G/A Polymorphism and Risk of Acne Vulgaris: A Meta-Analysis

PubMed Central

Yang, Jian-Kang; Wu, Wen-Juan; Qi, Jue; He, Li; Zhang, Ya-Ping

2014-01-01

Background The -308 G/A polymorphism in the tumor necrosis factor (TNF) gene has been implicated in the risk of acne vulgaris, but the results are inconclusive. The present meta-analysis aimed to investigate the overall association between the -308 G/A polymorphism and acne vulgaris risk. Methods We searched in Pubmed, Embase, Web of Science and CNKI for studies evaluating the association between the -308 G/A gene polymorphism and acne vulgaris risk. Data were extracted and statistical analysis was performed using STATA 12.0 software. Results A total of five publications involving 1553 subjects (728 acne vulgaris cases and 825 controls) were included in this meta-analysis. Combined analysis revealed a significant association between this polymorphism and acne vulgaris risk under recessive model (OR = 2.73, 95% CI: 1.37–5.44, p = 0.004 for AA vs. AG + GG). Subgroup analysis by ethnicity showed that the acne vulgaris risk associated with the -308 G/A gene polymorphism was significantly elevated among Caucasians under recessive model (OR = 2.34, 95% CI: 1.13–4.86, p = 0.023). Conclusion This meta-analysis suggests that the -308 G/A polymorphism in the TNF gene contributes to acne vulgaris risk, especially in Caucasian populations. Further studies among different ethnicity populations are needed to validate these findings. PMID:24498378

The Use of Object-Oriented Analysis Methods in Surety Analysis

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

Craft, Richard L.; Funkhouser, Donald R.; Wyss, Gregory D.

1999-05-01

Object-oriented analysis methods have been used in the computer science arena for a number of years to model the behavior of computer-based systems. This report documents how such methods can be applied to surety analysis. By embodying the causality and behavior of a system in a common object-oriented analysis model, surety analysts can make the assumptions that underlie their models explicit and thus better communicate with system designers. Furthermore, given minor extensions to traditional object-oriented analysis methods, it is possible to automatically derive a wide variety of traditional risk and reliability analysis methods from a single common object model. Automaticmore » model extraction helps ensure consistency among analyses and enables the surety analyst to examine a system from a wider variety of viewpoints in a shorter period of time. Thus it provides a deeper understanding of a system's behaviors and surety requirements. This report documents the underlying philosophy behind the common object model representation, the methods by which such common object models can be constructed, and the rules required to interrogate the common object model for derivation of traditional risk and reliability analysis models. The methodology is demonstrated in an extensive example problem.« less

Developing a suitable model for supplier selection based on supply chain risks: an empirical study from Iranian pharmaceutical companies.

PubMed

Mehralian, Gholamhossein; Rajabzadeh Gatari, Ali; Morakabati, Mohadese; Vatanpour, Hossein

2012-01-01

The supply chain represents the critical link between the development of new product and the market in pharmaceutical industry. Over the years, improvements made in supply chain operations have focused largely on ways to reduce cost and gain efficiencies in scale. In addition, powerful regulatory and market forces have provided new incentives for pharmaceutical firms to basically rethink the way they produce and distribute products, and also to re-imagine the role of the supply chain in driving strategic growth, brand differentiation and economic value in the health continuum. The purpose of this paper is to formulate basic factors involved in risk analysis of pharmaceutical industry, and also determine the effective factors involved in suppliers selection and their priorities. This paper is based on the results of literature review, experts' opinion acquisition, statistical analysis and also using MADM models on data gathered from distributed questionnaires. The model consists of the following steps and components: first factors involved in to supply chain risks are determined. Based on them a framework is considered. According the result of statistical analysis and MADM models the risk factors are formulated. The paper determines the main components and influenceial factors involving in the supply chain risks. Results showed that delivery risk can make an important contribution to mitigate the risk of pharmaceutical industry.

Developing a Suitable Model for Supplier Selection Based on Supply Chain Risks: An Empirical Study from Iranian Pharmaceutical Companies

PubMed Central

Mehralian, Gholamhossein; Rajabzadeh Gatari, Ali; Morakabati, Mohadese; Vatanpour, Hossein

2012-01-01

The supply chain represents the critical link between the development of new product and the market in pharmaceutical industry. Over the years, improvements made in supply chain operations have focused largely on ways to reduce cost and gain efficiencies in scale. In addition, powerful regulatory and market forces have provided new incentives for pharmaceutical firms to basically rethink the way they produce and distribute products, and also to re-imagine the role of the supply chain in driving strategic growth, brand differentiation and economic value in the health continuum. The purpose of this paper is to formulate basic factors involved in risk analysis of pharmaceutical industry, and also determine the effective factors involved in suppliers selection and their priorities. This paper is based on the results of literature review, experts’ opinion acquisition, statistical analysis and also using MADM models on data gathered from distributed questionnaires. The model consists of the following steps and components: first factors involved in to supply chain risks are determined. Based on them a framework is considered. According the result of statistical analysis and MADM models the risk factors are formulated. The paper determines the main components and influenceial factors involving in the supply chain risks. Results showed that delivery risk can make an important contribution to mitigate the risk of pharmaceutical industry. PMID:24250442

A model to predict the risk of lethal nasopharyngeal necrosis after re-irradiation with intensity-modulated radiotherapy in nasopharyngeal carcinoma patients.

PubMed

Yu, Ya-Hui; Xia, Wei-Xiong; Shi, Jun-Li; Ma, Wen-Juan; Li, Yong; Ye, Yan-Fang; Liang, Hu; Ke, Liang-Ru; Lv, Xing; Yang, Jing; Xiang, Yan-Qun; Guo, Xiang

2016-06-29

For patients with nasopharyngeal carcinoma (NPC) who undergo re-irradiation with intensity-modulated radiotherapy (IMRT), lethal nasopharyngeal necrosis (LNN) is a severe late adverse event. The purpose of this study was to identify risk factors for LNN and develop a model to predict LNN after radical re-irradiation with IMRT in patients with recurrent NPC. Patients who underwent radical re-irradiation with IMRT for locally recurrent NPC between March 2001 and December 2011 and who had no evidence of distant metastasis were included in this study. Clinical characteristics, including recurrent carcinoma conditions and dosimetric features, were evaluated as candidate risk factors for LNN. Logistic regression analysis was used to identify independent risk factors and construct the predictive scoring model. Among 228 patients enrolled in this study, 204 were at risk of developing LNN based on risk analysis. Of the 204 patients treated, 31 (15.2%) developed LNN. Logistic regression analysis showed that female sex (P = 0.008), necrosis before re-irradiation (P = 0.008), accumulated total prescription dose to the gross tumor volume (GTV) ≥145.5 Gy (P = 0.043), and recurrent tumor volume ≥25.38 cm(3) (P = 0.009) were independent risk factors for LNN. A model to predict LNN was then constructed that included these four independent risk factors. A model that includes sex, necrosis before re-irradiation, accumulated total prescription dose to GTV, and recurrent tumor volume can effectively predict the risk of developing LNN in NPC patients who undergo radical re-irradiation with IMRT.

Use of Model-Based Design Methods for Enhancing Resiliency Analysis of Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Knox, Lenora A.

The most common traditional non-functional requirement analysis is reliability. With systems becoming more complex, networked, and adaptive to environmental uncertainties, system resiliency has recently become the non-functional requirement analysis of choice. Analysis of system resiliency has challenges; which include, defining resilience for domain areas, identifying resilience metrics, determining resilience modeling strategies, and understanding how to best integrate the concepts of risk and reliability into resiliency. Formal methods that integrate all of these concepts do not currently exist in specific domain areas. Leveraging RAMSoS, a model-based reliability analysis methodology for Systems of Systems (SoS), we propose an extension that accounts for resiliency analysis through evaluation of mission performance, risk, and cost using multi-criteria decision-making (MCDM) modeling and design trade study variability modeling evaluation techniques. This proposed methodology, coined RAMSoS-RESIL, is applied to a case study in the multi-agent unmanned aerial vehicle (UAV) domain to investigate the potential benefits of a mission architecture where functionality to complete a mission is disseminated across multiple UAVs (distributed) opposed to being contained in a single UAV (monolithic). The case study based research demonstrates proof of concept for the proposed model-based technique and provides sufficient preliminary evidence to conclude which architectural design (distributed vs. monolithic) is most resilient based on insight into mission resilience performance, risk, and cost in addition to the traditional analysis of reliability.

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.

An Extreme-Value Approach to Anomaly Vulnerability Identification

NASA Technical Reports Server (NTRS)

Everett, Chris; Maggio, Gaspare; Groen, Frank

2010-01-01

The objective of this paper is to present a method for importance analysis in parametric probabilistic modeling where the result of interest is the identification of potential engineering vulnerabilities associated with postulated anomalies in system behavior. In the context of Accident Precursor Analysis (APA), under which this method has been developed, these vulnerabilities, designated as anomaly vulnerabilities, are conditions that produce high risk in the presence of anomalous system behavior. The method defines a parameter-specific Parameter Vulnerability Importance measure (PVI), which identifies anomaly risk-model parameter values that indicate the potential presence of anomaly vulnerabilities, and allows them to be prioritized for further investigation. This entails analyzing each uncertain risk-model parameter over its credible range of values to determine where it produces the maximum risk. A parameter that produces high system risk for a particular range of values suggests that the system is vulnerable to the modeled anomalous conditions, if indeed the true parameter value lies in that range. Thus, PVI analysis provides a means of identifying and prioritizing anomaly-related engineering issues that at the very least warrant improved understanding to reduce uncertainty, such that true vulnerabilities may be identified and proper corrective actions taken.

A comprehensive Network Security Risk Model for process control networks.

PubMed

Henry, Matthew H; Haimes, Yacov Y

2009-02-01

The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.

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...

Regulator of telomere elongation helicase 1 (RTEL1) rs6010620 polymorphism contribute to increased risk of glioma.

PubMed

Zhao, Wei; Bian, Yusong; Zhu, Wei; Zou, Peng; Tang, Guotai

2014-06-01

Regulator of telomere elongation helicase 1 (RTEL1) is critical for genome stability and tumor avoidance. Many studies have reported the associations of RTEL1 rs6010620 with glioma risk, but individually published results were inconclusive. This meta-analysis was performed to quantitatively summarize the evidence for such a relationship. The PubMed, Embase, and Web of Science were systematically searched to identify relevant studies. The odds ratio (OR) and 95 % confidence interval (95 % CI) were computed to estimate the strength of the association using a fixed or random effects model. Ten studies were eligible for meta-analysis including data on glioma with 6,490 cases and 9,288 controls. Overall, there was a significant association between RTEL1 rs6010620 polymorphism and glioma risk in all four genetic models (GG vs. AA: OR=1.87, 95 % CI=1.60-2.18, P heterogeneity=0.552; GA vs. AA: OR=1.30, 95 % CI=1.16-1.46, P heterogeneity=0.495; dominant model-GG+GA vs. AA: OR=1.46, 95 % CI=1.31-1.63, P heterogeneity=0.528; recessive model-GG vs. GA+AA: OR=1.36, 95 % CI=1.27-1.46, P heterogeneity=0.093). Subgroup analyses by ethnicity showed that RTEL1 rs6010620 polymorphism resulted in a higher risk of glioma among both Asians and Caucasians. In the stratified analysis by ethnicity and source of controls, significantly increased risk was observed for Asians and Europeans in all genetic models, population-based studies in all genetic models, and hospital-based studies in three genetic models (heterozygote comparison, homozygote comparison, and dominant model). Our meta-analysis suggested that RTEL1 rs6010620 polymorphism is likely to be associated with increased glioma risk, which lends further biological plausibility to these findings.

The correlation analysis of tumor necrosis factor-alpha-308G/A polymorphism and venous thromboembolism risk: A meta-analysis.

PubMed

Gao, Quangen; Zhang, Peijin; Wang, Wei; Ma, He; Tong, Yue; Zhang, Jing; Lu, Zhaojun

2016-10-01

Venous thromboembolism is a common complex disorder, being the resultant of gene-gene and gene-environment interactions. Tumor necrosis factor-alpha is a proinflammatory cytokine which has been implicated in venous thromboembolism risk. A promoter 308G/A polymorphism in the tumor necrosis factor-alpha gene has been suggested to modulate the risk for venous thromboembolism. However, the published findings remain inconsistent. In this study, we conducted a meta-analysis of all available data regarding this issue. Eligible studies were identified through search of Pubmed, EBSCO Medline, Web of Science, and China National Knowledge Infrastructure (CNKI, Chinese) databases up to June 2014. Pooled Odd ratios (ORs) with 95% confidence intervals were applied to estimating the strength of the genetic association in the random-effects model or fixed-effects model. A total of 10 studies involving 1999 venous thromboembolism cases and 2166 controls were included in this meta-analysis to evaluate the association between tumor necrosis factor-alpha-308G/A polymorphism and venous thromboembolism risk. Overall, no significantly increased risk venous thromboembolism was observed in all comparison models when all studies were pooled into the meta-analysis. However, in stratified analyses by ethnicity, there was a pronounced association with venous thromboembolism risk among West Asians in three genetic models (A vs. G: OR = 1.82, 95%CI = 1.13-2.94; GA vs. GG: OR = 1.82, 95%CI = 1.08-3.06; AA/GA vs. GG: OR = 1.88, 95%CI = 1.12-3.16). When stratifying by source of controls, no significant result was detected in all genetic models. This meta-analysis demonstrates that tumor necrosis factor-alpha 308G/A polymorphism may contribute to susceptibility to venous thromboembolism among West Asians. Studies are needed to ascertain these findings in larger samples and different racial groups. © The Author(s) 2015.

Paradigm of pretest risk stratification before coronary computed tomography.

PubMed

Jensen, Jesper Møller; Ovrehus, Kristian A; Nielsen, Lene H; Jensen, Jesper K; Larsen, Henrik M; Nørgaard, Bjarne L

2009-01-01

The optimal method of determining the pretest risk of coronary artery disease as a patient selection tool before coronary multidetector computed tomography (MDCT) is unknown. We investigated the ability of 3 different clinical risk scores to predict the outcome of coronary MDCT. This was a retrospective study of 551 patients consecutively referred for coronary MDCT on a suspicion of coronary artery disease. Diamond-Forrester, Duke, and Morise risk models were used to predict coronary artery stenosis (>50%) as assessed by coronary MDCT. The models were compared by receiver operating characteristic analysis. The distribution of low-, intermediate-, and high-risk persons, respectively, was established and compared for each of the 3 risk models. Overall, all risk prediction models performed equally well. However, the Duke risk model classified the low-risk patients more correctly than did the other models (P < 0.01). In patients without coronary artery calcification (CAC), the predictive value of the Duke risk model was superior to the other risk models (P < 0.05). Currently available risk prediction models seem to perform better in patients without CAC. Between the risk prediction models, there was a significant discrepancy in the distribution of patients at low, intermediate, or high risk (P < 0.01). The 3 risk prediction models perform equally well, although the Duke risk score may have advantages in subsets of patients. The choice of risk prediction model affects the referral pattern to MDCT. Copyright (c) 2009 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

External validation of the Garvan nomograms for predicting absolute fracture risk: the Tromsø study.

PubMed

Ahmed, Luai A; Nguyen, Nguyen D; Bjørnerem, Åshild; Joakimsen, Ragnar M; Jørgensen, Lone; Størmer, Jan; Bliuc, Dana; Center, Jacqueline R; Eisman, John A; Nguyen, Tuan V; Emaus, Nina

2014-01-01

Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort. The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance. The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture. The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction.

Development of Relative Risk Model for Regional Groundwater Risk Assessment: A Case Study in the Lower Liaohe River Plain, China

PubMed Central

Li, Xianbo; Zuo, Rui; Teng, Yanguo; Wang, Jinsheng; Wang, Bin

2015-01-01

Increasing pressure on water supply worldwide, especially in arid areas, has resulted in groundwater overexploitation and contamination, and subsequent deterioration of the groundwater quality and threats to public health. Environmental risk assessment of regional groundwater is an important tool for groundwater protection. This study presents a new approach for assessing the environmental risk assessment of regional groundwater. It was carried out with a relative risk model (RRM) coupled with a series of indices, such as a groundwater vulnerability index, which includes receptor analysis, risk source analysis, risk exposure and hazard analysis, risk characterization, and management of groundwater. The risk map is a product of the probability of environmental contamination and impact. The reliability of the RRM was verified using Monte Carlo analysis. This approach was applied to the lower Liaohe River Plain (LLRP), northeastern China, which covers 23604 km2. A spatial analysis tool within GIS which was used to interpolate and manipulate the data to develop environmental risk maps of regional groundwater, divided the level of risk from high to low into five ranks (V, IV, III, II, I). The results indicate that areas of relative risk rank (RRR) V cover 2324 km2, covering 9.8% of the area; RRR IV covers 3986 km2, accounting for 16.9% of the area. It is a new and appropriate method for regional groundwater resource management and land use planning, and is a rapid and effective tool for improving strategic decision making to protect groundwater and reduce environmental risk. PMID:26020518

Cost-effectiveness analysis of risk-reduction measures to reach water safety targets.

PubMed

Lindhe, Andreas; Rosén, Lars; Norberg, Tommy; Bergstedt, Olof; Pettersson, Thomas J R

2011-01-01

Identifying the most suitable risk-reduction measures in drinking water systems requires a thorough analysis of possible alternatives. In addition to the effects on the risk level, also the economic aspects of the risk-reduction alternatives are commonly considered important. Drinking water supplies are complex systems and to avoid sub-optimisation of risk-reduction measures, the entire system from source to tap needs to be considered. There is a lack of methods for quantification of water supply risk reduction in an economic context for entire drinking water systems. The aim of this paper is to present a novel approach for risk assessment in combination with economic analysis to evaluate risk-reduction measures based on a source-to-tap approach. The approach combines a probabilistic and dynamic fault tree method with cost-effectiveness analysis (CEA). The developed approach comprises the following main parts: (1) quantification of risk reduction of alternatives using a probabilistic fault tree model of the entire system; (2) combination of the modelling results with CEA; and (3) evaluation of the alternatives with respect to the risk reduction, the probability of not reaching water safety targets and the cost-effectiveness. The fault tree method and CEA enable comparison of risk-reduction measures in the same quantitative unit and consider costs and uncertainties. The approach provides a structured and thorough analysis of risk-reduction measures that facilitates transparency and long-term planning of drinking water systems in order to avoid sub-optimisation of available resources for risk reduction. Copyright © 2010 Elsevier Ltd. All rights reserved.

Which risk models perform best in selecting ever-smokers for lung cancer screening?

Cancer.gov

A new analysis by scientists at NCI evaluates nine different individualized lung cancer risk prediction models based on their selections of ever-smokers for computed tomography (CT) lung cancer screening.

An object-oriented approach to risk and reliability analysis : methodology and aviation safety applications.

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

Dandini, Vincent John; Duran, Felicia Angelica; Wyss, Gregory Dane

2003-09-01

This article describes how features of event tree analysis and Monte Carlo-based discrete event simulation can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology, with some of the best features of each. The resultant object-based event scenario tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible. Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST methodology is then applied to anmore » aviation safety problem that considers mechanisms by which an aircraft might become involved in a runway incursion incident. The resulting OBEST model demonstrates how a close link between human reliability analysis and probabilistic risk assessment methods can provide important insights into aviation safety phenomenology.« less

Using the weighted area under the net benefit curve for decision curve analysis.

PubMed

Talluri, Rajesh; Shete, Sanjay

2016-07-18

Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients. We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest. We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method. The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.

A modeling framework for exposing risks in complex systems.

PubMed

Sharit, J

2000-08-01

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

Information Processing and Risk Perception: An Adaptation of the Heuristic-Systematic Model.

ERIC Educational Resources Information Center

Trumbo, Craig W.

2002-01-01

Describes heuristic-systematic information-processing model and risk perception--the two major conceptual areas of the analysis. Discusses the proposed model, describing the context of the data collections (public health communication involving cancer epidemiology) and providing the results of a set of three replications using the proposed model.…

C282Y polymorphism in the HFE gene is associated with risk of breast cancer.

PubMed

Liu, Xiaoyan; Lv, Chunlei; Luan, Xiaorong; Lv, Ming

2013-10-01

The C282Y and H63D polymorphisms in the HFE gene have been implicated in susceptibility of breast cancer, but a number of studies have reported inconclusive results. The aim of this study is to investigate the association between the C282Y and H63D polymorphisms in the HFE gene and breast cancer risk by meta-analysis. We searched PubMed and Embase databases, covering all related studies until March 2, 2013. Statistical analysis was performed using STATA 10.0. A total of 7 studies including 1,720 cases and 18,296 controls for HFE C282Y polymorphism and 5 studies including 942 cases and 1,571 controls for HFE H63D polymorphism were included in the meta-analysis. The results showed that HFE C282Y polymorphism was significantly associated with increased risk of breast cancer under homozygotes vs. wild-type model (OR = 2.06, 95%CI = 1.19-3.58) and recessive model (OR = 1.98, 95%CI = 1.14-3.44) but not under heterozygotes vs. wild-type model (OR = 0.97, 95%CI = 0.70-1.35), dominant model (OR = 1.00, 95%CI = 0.72-1.40) and multiplicative model (OR = 1.04, 95%CI = 0.76-1.42). However, we did not find any association between HFE H63D polymorphism and breast cancer risk under all genetic models. This current meta-analysis suggested that C282Y polymorphism rather than H63D might be associated with increased risk of breast cancer.

Pooling-analysis on hMLH1 polymorphisms and cancer risk: evidence based on 31,484 cancer cases and 45,494 cancer-free controls.

PubMed

Li, Sha; Zheng, Yi; Tian, Tian; Wang, Meng; Liu, Xinghan; Liu, Kang; Zhai, Yajing; Dai, Cong; Deng, Yujiao; Li, Shanli; Dai, Zhijun; Lu, Jun

2017-11-03

To elucidate the veritable relationship between three hMLH1 polymorphisms (rs1800734, rs1799977, rs63750447) and cancer risk, we performed this meta-analysis based on overall published data up to May 2017, from PubMed, Web of knowledge, VIP, WanFang and CNKI database, and the references of the original studies or review articles. 57 publications including 31,484 cancer cases and 45,494 cancer-free controls were obtained. The quality assessment of six articles obtained a summarized score less than 6 in terms of the Newcastle-Ottawa Scale (NOS). All statistical analyses were calculated with the software STATA (Version 14.0; Stata Corp, College Station, TX). We found all the three polymorphisms can enhance overall cancer risk, especially in Asians, under different genetic comparisons. In the subgroup analysis by cancer type, we found a moderate association between rs1800734 and the risk of gastric cancer (allele model: OR = 1.14, P = 0.017; homozygote model: OR = 1.33, P = 0.019; dominant model: OR = 1.27, P = 0.024) and lung cancer in recessive model (OR = 1.27, P = 0.024). The G allele of rs1799977 polymorphism was proved to connect with susceptibility of colorectal cancer (allele model: OR = 1.21, P = 0.023; dominate model: OR = 1.32, P <0.0001) and prostate cancer (dominate model: OR = 1.36, P <0.0001). Rs63750447 showed an increased risk of colorectal cancer, endometrial cancer and gastric cancer under all genetic models. These findings provide evidence that hMLH1 polymorphisms may associate with cancer risk, especially in Asians.

An evaluation of Computational Fluid dynamics model for flood risk analysis

NASA Astrophysics Data System (ADS)

Di Francesco, Silvia; Biscarini, Chiara; Montesarchio, Valeria

2014-05-01

This work presents an analysis of the hydrological-hydraulic engineering requisites for Risk evaluation and efficient flood damage reduction plans. Most of the research efforts have been dedicated to the scientific and technical aspects of risk assessment, providing estimates of possible alternatives and of the risk associated. In the decision making process for mitigation plan, the contribute of scientist is crucial, due to the fact that Risk-Damage analysis is based on evaluation of flow field ,of Hydraulic Risk and on economical and societal considerations. The present paper will focus on the first part of process, the mathematical modelling of flood events which is the base for all further considerations. The evaluation of potential catastrophic damage consequent to a flood event and in particular to dam failure requires modelling of the flood with sufficient detail so to capture the spatial and temporal evolutions of the event, as well of the velocity field. Thus, the selection of an appropriate mathematical model to correctly simulate flood routing is an essential step. In this work we present the application of two 3D Computational fluid dynamics models to a synthetic and real case study in order to evaluate the correct evolution of flow field and the associated flood Risk . The first model is based on a opensource CFD platform called openFoam. Water flow is schematized with a classical continuum approach based on Navier-Stokes equation coupled with Volume of fluid (VOF) method to take in account the multiphase character of river bottom-water- air systems. The second model instead is based on the Lattice Boltzmann method, an innovative numerical fluid dynamics scheme based on Boltzmann's kinetic equation that represents the flow dynamics at the macroscopic level by incorporating a microscopic kinetic approach. Fluid is seen as composed by particles that can move and collide among them. Simulation results from both models are promising and congruent to experimental results available in literature, thought the LBM model requires less computational effort respect to the NS one.

"Know What to Do If You Encounter a Flash Flood": Mental Models Analysis for Improving Flash Flood Risk Communication and Public Decision Making.

PubMed

Lazrus, Heather; Morss, Rebecca E; Demuth, Julie L; Lazo, Jeffrey K; Bostrom, Ann

2016-02-01

Understanding how people view flash flood risks can help improve risk communication, ultimately improving outcomes. This article analyzes data from 26 mental models interviews about flash floods with members of the public in Boulder, Colorado, to understand their perspectives on flash flood risks and mitigation. The analysis includes a comparison between public and professional perspectives by referencing a companion mental models study of Boulder-area professionals. A mental models approach can help to diagnose what people already know about flash flood risks and responses, as well as any critical gaps in their knowledge that might be addressed through improved risk communication. A few public interviewees mentioned most of the key concepts discussed by professionals as important for flash flood warning decision making. However, most interviewees exhibited some incomplete understandings and misconceptions about aspects of flash flood development and exposure, effects, or mitigation that may lead to ineffective warning decisions when a flash flood threatens. These include important misunderstandings about the rapid evolution of flash floods, the speed of water in flash floods, the locations and times that pose the greatest flash flood risk in Boulder, the value of situational awareness and environmental cues, and the most appropriate responses when a flash flood threatens. The findings point to recommendations for ways to improve risk communication, over the long term and when an event threatens, to help people quickly recognize and understand threats, obtain needed information, and make informed decisions in complex, rapidly evolving extreme weather events such as flash floods. © 2015 Society for Risk Analysis.

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

PubMed

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

2016-09-01

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

Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

PubMed

Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

2016-10-01

The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

A Study of the Factors Associated with Risk for Development of Pressure Ulcers: A Longitudinal Analysis.

PubMed

Thomas, Elizebeth; Vinodkumar, Sudhaya; Mathew, Silvia; Setia, Maninder Singh

2015-01-01

Pressure ulcers (PUs) are prevalent in hospitalized patients; they may cause clinical, psychological, and economic problems in these patients. Previous studies are cross-sectional, have used pooled data, or cox-regression models to assess the risk for developing PU. However, PU risk scores change over time and models that account for time varying variables are useful for cohort analysis of data. The present longitudinal study was conducted to compare the risk of PU between surgical and nonsurgical patients, and to evaluate the factors associated with the development of these ulcers over a period of time. We evaluated 290 hospitalized patients over a 4 months period. The main outcomes for our analysis were: (1) Score on the pressure risk assessment scale; and (2) the proportion of individuals who were at severe risk for developing PUs. We used random effects models for longitudinal analysis of the data. The mean PU score was significantly higher in the nonsurgical patients compared with surgical patients at baseline (15.23 [3.86] vs. 9.33 [4.57]; P < 0.01). About 7% of the total patients had a score of >20 at baseline and were considered as being at high-risk for PU; the proportion was significantly higher among the nonsurgical patients compared with the surgical patients (14% vs. 4%, P = 0.003). In the adjusted models, there was no difference for severe risk for PU between surgical and nonsurgical patients (odds ratios [ORs]: 0.37, 95% confidence interval [CI]: 0.01-12.80). An additional day in the ward was associated with a significantly higher likelihood of being at high-risk for PU (OR: 1.47, 95% CI: 1.16-1.86). There were no significant differences between patients who were admitted for surgery compared with those who were not. An additional day in the ward, however, is important for developing a high-risk score for PU on the monitoring scale, and these patients require active interventions.

A Path Analysis of a Randomized "Promotora de Salud" Cardiovascular Disease-Prevention Trial among At-Risk Hispanic Adults

ERIC Educational Resources Information Center

de Heer, Hendrik Dirk; Balcazar, Hector G.; Castro, Felipe; Schulz, Leslie

2012-01-01

This study assessed effectiveness of an educational community intervention taught by "promotoras de salud" in reducing cardiovascular disease (CVD) risk among Hispanics using a structural equation modeling (SEM) approach. Model development was guided by a social ecological framework proposing CVD risk reduction through improvement of…

EVALUATION OF VADOSE ZONE AND SOURCE MODELS FOR MULTI-MEDIA, MULTI-PATHWAY, MULTI-RECEPTOR RISK ASSESSMENT USING LARGE SOIL COLUMN EXPERIMENT DATA

EPA Science Inventory

The U.S. Environmental Protection Agency (EPA) is developing a comprehensive environmental exposure and risk analysis software system for agency-wide application using the methodology of a Multi-media, Multi-pathway, Multi-receptor Risk Assessment (3MRA) model. This software sys...

Analysis of interactions among barriers in project risk management

NASA Astrophysics Data System (ADS)

Dandage, Rahul V.; Mantha, Shankar S.; Rane, Santosh B.; Bhoola, Vanita

2018-03-01

In the context of the scope, time, cost, and quality constraints, failure is not uncommon in project management. While small projects have 70% chances of success, large projects virtually have no chance of meeting the quadruple constraints. While there is no dearth of research on project risk management, the manifestation of barriers to project risk management is a less dwelt topic. The success of project management is oftentimes based on the understanding of barriers to effective risk management, application of appropriate risk management methodology, proactive leadership to avoid barriers, workers' attitude, adequate resources, organizational culture, and involvement of top management. This paper represents various risk categories and barriers to risk management in domestic and international projects through literature survey and feedback from project professionals. After analysing the various modelling methods used in project risk management literature, interpretive structural modelling (ISM) and MICMAC analysis have been used to analyse interactions among the barriers and prioritize them. The analysis indicates that lack of top management support, lack of formal training, and lack of addressing cultural differences are the high priority barriers, among many others.

Quantile uncertainty and value-at-risk model risk.

PubMed

Alexander, Carol; Sarabia, José María

2012-08-01

This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value-at-Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of "model risk" in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value-at-Risk model risk and compute the required regulatory capital add-on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value-at-Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks. © 2012 Society for Risk Analysis.

Quasi-likelihood generalized linear regression analysis of fatality risk data

DOT National Transportation Integrated Search

2009-01-01

Transportation-related fatality risks is a function of many interacting human, vehicle, and environmental factors. Statisitcally valid analysis of such data is challenged both by the complexity of plausable structural models relating fatality rates t...

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

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

Smith, Curtis L; Mandelli, Diego; Zhegang Ma

2014-11-01

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

A Framework for Flood Risk Analysis and Benefit Assessment of Flood Control Measures in Urban Areas

PubMed Central

Li, Chaochao; Cheng, Xiaotao; Li, Na; Du, Xiaohe; Yu, Qian; Kan, Guangyuan

2016-01-01

Flood risk analysis is more complex in urban areas than that in rural areas because of their closely packed buildings, different kinds of land uses, and large number of flood control works and drainage systems. The purpose of this paper is to propose a practical framework for flood risk analysis and benefit assessment of flood control measures in urban areas. Based on the concept of disaster risk triangle (hazard, vulnerability and exposure), a comprehensive analysis method and a general procedure were proposed for urban flood risk analysis. Urban Flood Simulation Model (UFSM) and Urban Flood Damage Assessment Model (UFDAM) were integrated to estimate the flood risk in the Pudong flood protection area (Shanghai, China). S-shaped functions were adopted to represent flood return period and damage (R-D) curves. The study results show that flood control works could significantly reduce the flood risk within the 66-year flood return period and the flood risk was reduced by 15.59%. However, the flood risk was only reduced by 7.06% when the flood return period exceeded 66-years. Hence, it is difficult to meet the increasing demands for flood control solely relying on structural measures. The R-D function is suitable to describe the changes of flood control capacity. This frame work can assess the flood risk reduction due to flood control measures, and provide crucial information for strategy development and planning adaptation. PMID:27527202

Dose-Dependent Associations between Wine Drinking and Breast Cancer Risk - Meta-Analysis Findings.

PubMed

Chen, Jia-Yan; Zhu, Hong-Cheng; Guo, Qing; Shu, Zheng; Bao, Xu-Hui; Sun, Feng; Qin, Qin; Yang, Xi; Zhang, Chi; Cheng, Hong-Yan; Sun, Xin-Chen

2016-01-01

To investigate any potential association between wine and breast cancer risk. We quantitatively assessed associations by conducting a meta-analysis based on evidence from observational studies. In May 2014, we performed electronic searches in PubMed, EmBase and the Cochrane Library to identify studies examining the effect of wine drinking on breast cancer incidence. The relative risk (RR) or odds ratio (OR) were used to measure any such association. The analysis was further stratified by confounding factors that could influence the results. A total of twenty-six studies (eight case-control and eighteen cohort studies) involving 21,149 cases were included in our meta-analysis. Our study demonstrated that wine drinking was associated with breast cancer risk. A 36% increase in breast cancer risk was observed across overall studies based on the highest versus lowest model, with a combined RR of 1.0059 (95%CI 0.97-1.05) in dose-response analysis. However, 5 g/d ethanol from wine seemed to have protective value from our non-linear model. Our findings indicate that wine drinking is associated with breast cancer risk in a dose-dependent manner. High consumption of wine contributes to breast cancer risk with protection exerted by low doses. Further investigations are needed for clarification.

Evaluation of Contamination Inspection and Analysis Methods through Modeling System Performance

NASA Technical Reports Server (NTRS)

Seasly, Elaine; Dever, Jason; Stuban, Steven M. F.

2016-01-01

Contamination is usually identified as a risk on the risk register for sensitive space systems hardware. Despite detailed, time-consuming, and costly contamination control efforts during assembly, integration, and test of space systems, contaminants are still found during visual inspections of hardware. Improved methods are needed to gather information during systems integration to catch potential contamination issues earlier and manage contamination risks better. This research explores evaluation of contamination inspection and analysis methods to determine optical system sensitivity to minimum detectable molecular contamination levels based on IEST-STD-CC1246E non-volatile residue (NVR) cleanliness levels. Potential future degradation of the system is modeled given chosen modules representative of optical elements in an optical system, minimum detectable molecular contamination levels for a chosen inspection and analysis method, and determining the effect of contamination on the system. By modeling system performance based on when molecular contamination is detected during systems integration and at what cleanliness level, the decision maker can perform trades amongst different inspection and analysis methods and determine if a planned method is adequate to meet system requirements and manage contamination risk.

A statistical model of operational impacts on the framework of the bridge crane

NASA Astrophysics Data System (ADS)

Antsev, V. Yu; Tolokonnikov, A. S.; Gorynin, A. D.; Reutov, A. A.

2017-02-01

The technical regulations of the Customs Union demands implementation of the risk analysis of the bridge cranes operation at their design stage. The statistical model has been developed for performance of random calculations of risks, allowing us to model possible operational influences on the bridge crane metal structure in their various combination. The statistical model is practically actualized in the software product automated calculation of risks of failure occurrence of bridge cranes.

Combined Hydrologic (AGWA-KINEROS2) and Hydraulic (HEC2) Modeling for Post-Fire Runoff and Inundation Risk Assessment through a Set of Python Tools

NASA Astrophysics Data System (ADS)

Barlow, J. E.; Goodrich, D. C.; Guertin, D. P.; Burns, I. S.

2016-12-01

Wildfires in the Western United States can alter landscapes by removing vegetation and changing soil properties. These altered landscapes produce more runoff than pre-fire landscapes which can lead to post-fire flooding that can damage infrastructure and impair natural resources. Resources, structures, historical artifacts and others that could be impacted by increased runoff are considered values at risk. .The Automated Geospatial Watershed Assessment tool (AGWA) allows users to quickly set up and execute the Kinematic Runoff and Erosion model (KINEROS2 or K2) in the ESRI ArcMap environment. The AGWA-K2 workflow leverages the visualization capabilities of GIS to facilitate evaluation of rapid watershed assessments for post-fire planning efforts. High relative change in peak discharge, as simulated by K2, provides a visual and numeric indicator to investigate those channels in the watershed that should be evaluated for more detailed analysis, especially if values at risk are within or near that channel. Modeling inundation extent along a channel would provide more specific guidance about risk along a channel. HEC-2 and HEC-RAS can be used for hydraulic modeling efforts at the reach and river system scale. These models have been used to address flood boundaries and, accordingly, flood risk. However, data collection and organization for hydraulic models can be time consuming and therefore a combined hydrologic-hydraulic modeling approach is not often employed for rapid assessments. A simplified approach could streamline this process and provide managers with a simple workflow and tool to perform a quick risk assessment for a single reach. By focusing on a single reach highlighted by large relative change in peak discharge, data collection efforts can be minimized and the hydraulic computations can be performed to supplement risk analysis. The incorporation of hydraulic analysis through a suite of Python tools (as outlined by HEC-2) with AGWA-K2 will allow more rapid applications of combined hydrologic-hydraulic modeling. This combined modeling approach is built in the ESRI ArcGIS application to enable rapid model preparation, execution and result visualization for risk assessment in post-fire environments.

The Research on the Factors of Purchase Intention for Fresh Agricultural Products in an E-Commerce Environment

NASA Astrophysics Data System (ADS)

Han, Dan; Mu, Jing

2017-12-01

Based on the characteristics of e-commerce of fresh agricultural products in China, and using the correlation analysis method, the relational model between product knowledge, perceived benefit, perceived risk and purchase intention is constructed. The Logistic model is used to carry in the empirical analysis. The influence factors and the mechanism of online purchase intention are explored. The results show that consumers’ product knowledge, perceived benefit and perceived risk can affect their purchase intention. Consumers’ product knowledge has a positive effect on perceived benefit and perceived benefit has a positive effect on purchase intention. Consumers’ product knowledge has a negative effect on perceived risk, and perceived profit has a negative effect on perceived risk, and perceived risk has a negative effect on purchase intention. Through the empirical analysis, some feasible suggestions for the government and electricity supplier enterprises can be provided.

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

PubMed

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

2017-05-01

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

Influence of Methylenetetrahydrofolate Reductase C677T Polymorphism on the Risk of Lung Cancer and the Clinical Response to Platinum-Based Chemotherapy for Advanced Non-Small Cell Lung Cancer: An Updated Meta-Analysis

PubMed Central

Zhu, Ning; Gong, Yi; He, Jian; Xia, Jingwen

2013-01-01

Purpose Methylenetetrahydrofolate reductase (MTHFR) has been implicated in lung cancer risk and response to platinum-based chemotherapy in advanced non-small cell lung cancer (NSCLC). However, the results are controversial. We performed meta-analysis to investigate the effect of MTHFR C677T polymorphism on lung cancer risk and response to platinum-based chemotherapy in advanced NSCLC. Materials and Methods The databases of PubMed, Ovid, Wanfang and Chinese Biomedicine were searched for eligible studies. Nineteen studies on MTHFR C677T polymorphism and lung cancer risk and three articles on C677T polymorphism and response to platinum-based chemotherapy in advanced NSCLC, were identified. Results The results indicated that the allelic contrast, homozygous contrast and recessive model of the MTHFR C677T polymorphism were associated significantly with increased lung cancer risk. In the subgroup analysis, the C677T polymorphism was significantly correlated with an increased risk of NSCLC, with the exception of the recessive model. The dominant model and the variant T allele showed a significant association with lung cancer susceptibility of ever smokers. Male TT homozygote carriers had a higher susceptibility, but the allelic contrast and homozygote model had a protective effect in females. No relationship was observed for SCLC in any comparison model. In addition, MTHFR 677TT homozygote carriers had a better response to platinum-based chemotherapy in advanced NSCLC in the recessive model. Conclusion The MTHFR C677T polymorphism might be a genetic marker for lung cancer risk or response to platinum-based chemotherapy in advanced NSCLC. However, our results require further verification. PMID:24142642

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.

Simulation Assisted Risk Assessment: Blast Overpressure Modeling

NASA Technical Reports Server (NTRS)

Lawrence, Scott L.; Gee, Ken; Mathias, Donovan; Olsen, Michael

2006-01-01

A probabilistic risk assessment (PRA) approach has been developed and applied to the risk analysis of capsule abort during ascent. The PRA is used to assist in the identification of modeling and simulation applications that can significantly impact the understanding of crew risk during this potentially dangerous maneuver. The PRA approach is also being used to identify the appropriate level of fidelity for the modeling of those critical failure modes. The Apollo launch escape system (LES) was chosen as a test problem for application of this approach. Failure modes that have been modeled and/or simulated to date include explosive overpressure-based failure, explosive fragment-based failure, land landing failures (range limits exceeded either near launch or Mode III trajectories ending on the African continent), capsule-booster re-contact during separation, and failure due to plume-induced instability. These failure modes have been investigated using analysis tools in a variety of technical disciplines at various levels of fidelity. The current paper focuses on the development and application of a blast overpressure model for the prediction of structural failure due to overpressure, including the application of high-fidelity analysis to predict near-field and headwinds effects.

Risk analysis of new oral anticoagulants for gastrointestinal bleeding and intracranial hemorrhage in atrial fibrillation patients: a systematic review and network meta-analysis.

PubMed

Xu, Wei-Wei; Hu, Shen-Jiang; Wu, Tao

2017-07-01

Antithrombotic therapy using new oral anticoagulants (NOACs) in patients with atrial fibrillation (AF) has been generally shown to have a favorable risk-benefit profile. Since there has been dispute about the risks of gastrointestinal bleeding (GIB) and intracranial hemorrhage (ICH), we sought to conduct a systematic review and network meta-analysis using Bayesian inference to analyze the risks of GIB and ICH in AF patients taking NOACs. We analyzed data from 20 randomized controlled trials of 91 671 AF patients receiving anticoagulants, antiplatelet drugs, or placebo. Bayesian network meta-analysis of two different evidence networks was performed using a binomial likelihood model, based on a network in which different agents (and doses) were treated as separate nodes. Odds ratios (ORs) and 95% confidence intervals (CIs) were modeled using Markov chain Monte Carlo methods. Indirect comparisons with the Bayesian model confirmed that aspirin+clopidogrel significantly increased the risk of GIB in AF patients compared to the placebo (OR 0.33, 95% CI 0.01-0.92). Warfarin was identified as greatly increasing the risk of ICH compared to edoxaban 30 mg (OR 3.42, 95% CI 1.22-7.24) and dabigatran 110 mg (OR 3.56, 95% CI 1.10-8.45). We further ranked the NOACs for the lowest risk of GIB (apixaban 5 mg) and ICH (apixaban 5 mg, dabigatran 110 mg, and edoxaban 30 mg). Bayesian network meta-analysis of treatment of non-valvular AF patients with anticoagulants suggested that NOACs do not increase risks of GIB and/or ICH, compared to each other.

Strengthening the weak link: Built Environment modelling for loss analysis

NASA Astrophysics Data System (ADS)

Millinship, I.

2012-04-01

Methods to analyse insured losses from a range of natural perils, including pricing by primary insurers and catastrophe modelling by reinsurers, typically lack sufficient exposure information. Understanding the hazard intensity in terms of spatial severity and frequency is only the first step towards quantifying the risk of a catastrophic event. For any given event we need to know: Are any structures affected? What type of buildings are they? How much damaged occurred? How much will the repairs cost? To achieve this, detailed exposure information is required to assess the likely damage and to effectively calculate the resultant loss. Modelling exposures in the Built Environment therefore plays as important a role in understanding re/insurance risk as characterising the physical hazard. Across both primary insurance books and aggregated reinsurance portfolios, the location of a property (a risk) and its monetary value is typically known. Exactly what that risk is in terms of detailed property descriptors including structure type and rebuild cost - and therefore its vulnerability to loss - is often omitted. This data deficiency is a primary source of variations between modelled losses and the actual claims value. Built Environment models are therefore required at a high resolution to describe building attributes that relate vulnerability to property damage. However, national-scale household-level datasets are often not computationally practical in catastrophe models and data must be aggregated. In order to provide more accurate risk analysis, we have developed and applied a methodology for Built Environment modelling for incorporation into a range of re/insurance applications, including operational models for different international regions and different perils and covering residential, commercial and industry exposures. Illustrated examples are presented, including exposure modelling suitable for aggregated reinsurance analysis for the UK and bespoke high resolution modelling for industrial sites in Germany. A range of attributes are included following detailed claims analysis and engineering research with property type, age and condition identified as important differentiators of damage from flood, wind and freeze events.

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.

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.

Predicting Falls and When to Intervene in Older People: A Multilevel Logistical Regression Model and Cost Analysis

PubMed Central

Smith, Matthew I.; de Lusignan, Simon; Mullett, David; Correa, Ana; Tickner, Jermaine; Jones, Simon

2016-01-01

Introduction Falls are the leading cause of injury in older people. Reducing falls could reduce financial pressures on health services. We carried out this research to develop a falls risk model, using routine primary care and hospital data to identify those at risk of falls, and apply a cost analysis to enable commissioners of health services to identify those in whom savings can be made through referral to a falls prevention service. Methods Multilevel logistical regression was performed on routinely collected general practice and hospital data from 74751 over 65’s, to produce a risk model for falls. Validation measures were carried out. A cost-analysis was performed to identify at which level of risk it would be cost-effective to refer patients to a falls prevention service. 95% confidence intervals were calculated using a Monte Carlo Model (MCM), allowing us to adjust for uncertainty in the estimates of these variables. Results A risk model for falls was produced with an area under the curve of the receiver operating characteristics curve of 0.87. The risk cut-off with the highest combination of sensitivity and specificity was at p = 0.07 (sensitivity of 81% and specificity of 78%). The risk cut-off at which savings outweigh costs was p = 0.27 and the risk cut-off with the maximum savings was p = 0.53, which would result in referral of 1.8% and 0.45% of the over 65’s population respectively. Above a risk cut-off of p = 0.27, costs do not exceed savings. Conclusions This model is the best performing falls predictive tool developed to date; it has been developed on a large UK city population; can be readily run from routine data; and can be implemented in a way that optimises the use of health service resources. Commissioners of health services should use this model to flag and refer patients at risk to their falls service and save resources. PMID:27448280

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

EPA Science Inventory

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

XPA A23G polymorphism and risk of digestive system cancers: a meta-analysis.

PubMed

He, Lei; Deng, Tao; Luo, Hesheng

2015-01-01

Several studies have reported an association between the A23G polymorphism (rs 1800975) in the xeroderma pigmentosum group A (XPA) gene and risk of digestive system cancers. However, the results are inconsistent. In this study, we performed a meta-analysis to assess the association between XPA A23G polymorphism and the risk of digestive system cancers. Relevant studies were identified using the PubMed, Web of Science, China National Knowledge Infrastructure, WanFang, and VIP databases up to August 30, 2014. The pooled odds ratio (OR) with a 95% confidence interval (CI) was calculated using the fixed or random effects model. A total of 18 case-control studies from 16 publications with 4,170 patients and 6,929 controls were included. Overall, no significant association was found between XPA A23G polymorphism and the risk of digestive system cancers (dominant model: GA + AA versus GG, OR 0.89, 95% CI 0.74-1.08; recessive model: AA versus GA + GG, OR 0.94, 95% CI 0.74-1.20; GA versus GG, OR 0.89, 95% CI 0.77-1.03; and AA versus GG, OR 0.87, 95% CI 0.64-1.19). When the analysis was stratified by ethnicity, similar results were observed among Asians and Caucasians in all genetic models. In stratified analysis based on tumor type, we also failed to detect any association between XPA A23G polymorphism and the risk of esophageal, gastric, or colorectal cancers. This meta-analysis indicates that the XPA A23G polymorphism is not associated with a risk of digestive system cancers.

Space Shuttle critical function audit

NASA Technical Reports Server (NTRS)

Sacks, Ivan J.; Dipol, John; Su, Paul

1990-01-01

A large fault-tolerance model of the main propulsion system of the US space shuttle has been developed. This model is being used to identify single components and pairs of components that will cause loss of shuttle critical functions. In addition, this model is the basis for risk quantification of the shuttle. The process used to develop and analyze the model is digraph matrix analysis (DMA). The DMA modeling and analysis process is accessed via a graphics-based computer user interface. This interface provides coupled display of the integrated system schematics, the digraph models, the component database, and the results of the fault tolerance and risk analyses.

Review and developments of dissemination models for airborne carbon fibers

NASA Technical Reports Server (NTRS)

Elber, W.

1980-01-01

Dissemination prediction models were reviewed to determine their applicability to a risk assessment for airborne carbon fibers. The review showed that the Gaussian prediction models using partial reflection at the ground agreed very closely with a more elaborate diffusion analysis developed for the study. For distances beyond 10,000 m the Gaussian models predicted a slower fall-off in exposure levels than the diffusion models. This resulting level of conservatism was preferred for the carbon fiber risk assessment. The results also showed that the perfect vertical-mixing models developed herein agreed very closely with the diffusion analysis for all except the most stable atmospheric conditions.

A Serological Biopsy Using Five Stomach-Specific Circulating Biomarkers for Gastric Cancer Risk Assessment: A Multi-Phase Study.

PubMed

Tu, Huakang; Sun, Liping; Dong, Xiao; Gong, Yuehua; Xu, Qian; Jing, Jingjing; Bostick, Roberd M; Wu, Xifeng; Yuan, Yuan

2017-05-01

We aimed to assess a serological biopsy using five stomach-specific circulating biomarkers-pepsinogen I (PGI), PGII, PGI/II ratio, anti-Helicobacter pylori (H. pylori) antibody, and gastrin-17 (G-17)-for identifying high-risk individuals and predicting risk of developing gastric cancer (GC). Among 12,112 participants with prospective follow-up from an ongoing population-based screening program using both serology and gastroscopy in China, we conducted a multi-phase study involving a cross-sectional analysis, a follow-up analysis, and an integrative risk prediction modeling analysis. In the cross-sectional analysis, the five biomarkers (especially PGII, the PGI/II ratio, and H. pylori sero-positivity) were associated with the presence of precancerous gastric lesions or GC at enrollment. In the follow-up analysis, low PGI levels and PGI/II ratios were associated with higher risk of developing GC, and both low (<0.5 pmol/l) and high (>4.7 pmol/l) G-17 levels were associated with higher risk of developing GC, suggesting a J-shaped association. In the risk prediction modeling analysis, the five biomarkers combined yielded a C statistic of 0.803 (95% confidence interval (CI)=0.789-0.816) and improved prediction beyond traditional risk factors (C statistic from 0.580 to 0.811, P<0.001) for identifying precancerous lesions at enrollment, and higher serological biopsy scores based on the five biomarkers at enrollment were associated with higher risk of developing GC during follow-up (P for trend <0.001). A serological biopsy composed of the five stomach-specific circulating biomarkers could be used to identify high-risk individuals for further diagnostic gastroscopy, and to stratify individuals' risk of developing GC and thus to guide targeted screening and precision prevention.

University of North Carolina Caries Risk Assessment Study: comparisons of high risk prediction, any risk prediction, and any risk etiologic models.

PubMed

Beck, J D; Weintraub, J A; Disney, J A; Graves, R C; Stamm, J W; Kaste, L M; Bohannan, H M

1992-12-01

The purpose of this analysis is to compare three different statistical models for predicting children likely to be at risk of developing dental caries over a 3-yr period. Data are based on 4117 children who participated in the University of North Carolina Caries Risk Assessment Study, a longitudinal study conducted in the Aiken, South Carolina, and Portland, Maine areas. The three models differed with respect to either the types of variables included or the definition of disease outcome. The two "Prediction" models included both risk factor variables thought to cause dental caries and indicator variables that are associated with dental caries, but are not thought to be causal for the disease. The "Etiologic" model included only etiologic factors as variables. A dichotomous outcome measure--none or any 3-yr increment, was used in the "Any Risk Etiologic model" and the "Any Risk Prediction Model". Another outcome, based on a gradient measure of disease, was used in the "High Risk Prediction Model". The variables that are significant in these models vary across grades and sites, but are more consistent among the Etiologic model than the Predictor models. However, among the three sets of models, the Any Risk Prediction Models have the highest sensitivity and positive predictive values, whereas the High Risk Prediction Models have the highest specificity and negative predictive values. Considerations in determining model preference are discussed.

Deterrence and Risk Preferences in Sequential Attacker-Defender Games with Continuous Efforts.

PubMed

Payyappalli, Vineet M; Zhuang, Jun; Jose, Victor Richmond R

2017-11-01

Most attacker-defender games consider players as risk neutral, whereas in reality attackers and defenders may be risk seeking or risk averse. This article studies the impact of players' risk preferences on their equilibrium behavior and its effect on the notion of deterrence. In particular, we study the effects of risk preferences in a single-period, sequential game where a defender has a continuous range of investment levels that could be strategically chosen to potentially deter an attack. This article presents analytic results related to the effect of attacker and defender risk preferences on the optimal defense effort level and their impact on the deterrence level. Numerical illustrations and some discussion of the effect of risk preferences on deterrence and the utility of using such a model are provided, as well as sensitivity analysis of continuous attack investment levels and uncertainty in the defender's beliefs about the attacker's risk preference. A key contribution of this article is the identification of specific scenarios in which the defender using a model that takes into account risk preferences would be better off than a defender using a traditional risk-neutral model. This study provides insights that could be used by policy analysts and decisionmakers involved in investment decisions in security and safety. © 2017 Society for Risk Analysis.

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

NASA Astrophysics Data System (ADS)

Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

2016-03-01

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

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

PubMed

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

2016-06-01

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

Literature Review on Modeling Cyber Networks and Evaluating Cyber Risks.

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

Kelic, Andjelka; Campbell, Philip L

The National Infrastructure Simulations and Analysis Center (NISAC) conducted a literature review on modeling cyber networks and evaluating cyber risks. The literature review explores where modeling is used in the cyber regime and ways that consequence and risk are evaluated. The relevant literature clusters in three different spaces: network security, cyber-physical, and mission assurance. In all approaches, some form of modeling is utilized at varying levels of detail, while the ability to understand consequence varies, as do interpretations of risk. This document summarizes the different literature viewpoints and explores their applicability to securing enterprise networks.

Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials.

PubMed

Hristozov, Danail; Zabeo, Alex; Alstrup Jensen, Keld; Gottardo, Stefania; Isigonis, Panagiotis; Maccalman, Laura; Critto, Andrea; Marcomini, Antonio

2016-11-01

Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables.

Geothermal Play-Fairway Analysis of the Tatun Volcano Group, Taiwan

NASA Astrophysics Data System (ADS)

Chen, Yan-Ru; Song, Sheng-Rong

2017-04-01

Geothermal energy is a sustainable and low-emission energy resource. It has the advantage of low-cost and withstanding nature hazards. Taiwan is located on the western Ring of Fire and characteristic of widespread hot spring and high surface heat flows, especially on the north of Taiwan. Many previous studies reveal that the Tatun Volcano Group (TVG) has great potential to develop the geothermal energy. However, investment in geothermal development has inherent risk and how to reduce the exploration risk is the most important. The exploration risk can be lowered by using the play-fairway analysis (PFA) that integrates existing data representing the composite risk segments in the region in order to define the exploration strategy. As a result, this study has adapted this logic for geothermal exploration in TVG. There are two necessary factors in geothermal energy, heat and permeability. They are the composite risk segments for geothermal play-fairway analysis. This study analyzes existing geologic, geophysical and geochemical data to construct the heat and permeability potential models. Heat potential model is based on temperature gradient, temperature of hot spring, proximity to hot spring, hydrothermal alteration zones, helium isotope ratios, and magnetics. Permeability potential model is based on fault zone, minor fault, and micro-earthquake activities. Then, these two potential models are weighted by using the Analytical Hierarchy Process (AHP) and combined to rank geothermal favorability. Uncertainty model is occurred by the quality of data and spatial accuracy of data. The goal is to combine the potential model with the uncertainty model as a risk map to find the best drilling site for geothermal exploration in TVG. Integrated results indicate where geothermal potential is the highest and provide the best information for those who want to develop the geothermal exploration in TVG.

Large Scale Flood Risk Analysis using a New Hyper-resolution Population Dataset

NASA Astrophysics Data System (ADS)

Smith, A.; Neal, J. C.; Bates, P. D.; Quinn, N.; Wing, O.

2017-12-01

Here we present the first national scale flood risk analyses, using high resolution Facebook Connectivity Lab population data and data from a hyper resolution flood hazard model. In recent years the field of large scale hydraulic modelling has been transformed by new remotely sensed datasets, improved process representation, highly efficient flow algorithms and increases in computational power. These developments have allowed flood risk analysis to be undertaken in previously unmodeled territories and from continental to global scales. Flood risk analyses are typically conducted via the integration of modelled water depths with an exposure dataset. Over large scales and in data poor areas, these exposure data typically take the form of a gridded population dataset, estimating population density using remotely sensed data and/or locally available census data. The local nature of flooding dictates that for robust flood risk analysis to be undertaken both hazard and exposure data should sufficiently resolve local scale features. Global flood frameworks are enabling flood hazard data to produced at 90m resolution, resulting in a mis-match with available population datasets which are typically more coarsely resolved. Moreover, these exposure data are typically focused on urban areas and struggle to represent rural populations. In this study we integrate a new population dataset with a global flood hazard model. The population dataset was produced by the Connectivity Lab at Facebook, providing gridded population data at 5m resolution, representing a resolution increase over previous countrywide data sets of multiple orders of magnitude. Flood risk analysis undertaken over a number of developing countries are presented, along with a comparison of flood risk analyses undertaken using pre-existing population datasets.

Three ADIPOR1 Polymorphisms and Cancer Risk: A Meta-Analysis of Case-Control Studies.

PubMed

Ye, Jiaxiang; Jiang, Li; Wu, Changliang; Liu, Aiqun; Mao, Sufei; Ge, Lianying

2015-01-01

Studies have come to conflicting conclusions about whether polymorphisms in the adiponectin receptor 1 gene (ADIPOR1) are associated with cancer risk. To help resolve this question, we meta-analyzed case-control studies in the literature. PubMed, EMBASE, Cochrane Library, the Chinese Biological Medical Database and the Chinese National Knowledge Infrastructure Database were systematically searched to identify all case-control studies published through February 2015 examining any ADIPOR1 polymorphisms and risk of any type of cancer. Pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated. A total of 13 case-control studies involving 5,750 cases and 6,762 controls were analyzed. Analysis of the entire study population revealed a significant association between rs1342387(G/A) and overall cancer risk using a homozygous model (OR 0.82, 95%CI 0.72 to 0.94), heterozygous model (OR 0.84, 95%CI 0.76 to 0.93), dominant model (OR 0.85, 95%CI 0.75 to 0.97) and allele contrast model (OR 0.88, 95%CI 0.80 to 0.97). However, subgroup analysis showed that this association was significant only for Asians in the case of colorectal cancer. No significant associations were found between rs12733285(C/T) or rs7539542(C/G) and cancer risk, either in analyses of the entire study population or in analyses of subgroups. Our meta-analysis suggests that the ADIPOR1 rs1342387(G/A) polymorphism, but not rs12733285(C/T) or rs7539542(C/G), may be associated with cancer risk, especially risk of colorectal cancer in Asians. Large, well-designed studies are needed to verify our findings.

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

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

Szilard, Ronaldo Henriques

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

Risks associated with antiretroviral treatment for human immunodeficiency virus (HIV): qualitative analysis of social media data and health state utility valuation.

PubMed

Matza, Louis S; Chung, Karen C; Kim, Katherine J; Paulus, Trena M; Davies, Evan W; Stewart, Katie D; McComsey, Grace A; Fordyce, Marshall W

2017-07-01

Despite benefits of antiretroviral therapies (ART), people with HIV infection have increased risk of cardiovascular disease, kidney disease, and low bone mineral density. Some ARTs increase risk of these events. The purpose of this study was to examine patients' perspectives of these risks and estimate health state utilities associated with these risks for use in cost-utility models. Qualitative thematic analysis was conducted to examine messages posted to the POZ/AIDSmeds Internet community forums, focusing on bone, kidney, and cardiovascular side effects and risks of HIV/AIDS medications. Then, health state vignettes were drafted based on this qualitative analysis, literature review, and clinician interviews. The health states (representing HIV, plus treatment-related risks) were valued in time trade-off interviews with general population participants in the UK. Qualitative analysis of the Internet forums documented patient concerns about ART risks, as well as treatment decisions made because of these risks. A total of 208 participants completed utility interviews (51.4% female; mean age 44.6 years). The mean utility of the HIV health state (virologically suppressed, treated with ART) was 0.86. Adding a description of risk resulted in statistically significant disutility (i.e., utility decreases): renal risk (disutility = -0.02), bone risk (-0.03), and myocardial infarction risk (-0.05). Patient concerns and treatment decisions were documented via qualitative analysis of Internet forum discussions, and the impact of these concerns was quantified in terms of health state utilities. The resulting disutilities may be useful for differentiating among ARTs in economic modeling of treatment for patients with HIV.

Applicability and feasibility of systematic review for performing evidence-based risk assessment in food and feed safety.

PubMed

Aiassa, E; Higgins, J P T; Frampton, G K; Greiner, M; Afonso, A; Amzal, B; Deeks, J; Dorne, J-L; Glanville, J; Lövei, G L; Nienstedt, K; O'connor, A M; Pullin, A S; Rajić, A; Verloo, D

2015-01-01

Food and feed safety risk assessment uses multi-parameter models to evaluate the likelihood of adverse events associated with exposure to hazards in human health, plant health, animal health, animal welfare, and the environment. Systematic review and meta-analysis are established methods for answering questions in health care, and can be implemented to minimize biases in food and feed safety risk assessment. However, no methodological frameworks exist for refining risk assessment multi-parameter models into questions suitable for systematic review, and use of meta-analysis to estimate all parameters required by a risk model may not be always feasible. This paper describes novel approaches for determining question suitability and for prioritizing questions for systematic review in this area. Risk assessment questions that aim to estimate a parameter are likely to be suitable for systematic review. Such questions can be structured by their "key elements" [e.g., for intervention questions, the population(s), intervention(s), comparator(s), and outcome(s)]. Prioritization of questions to be addressed by systematic review relies on the likely impact and related uncertainty of individual parameters in the risk model. This approach to planning and prioritizing systematic review seems to have useful implications for producing evidence-based food and feed safety risk assessment.

Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

PubMed

Lee, Wen-Chung; Wu, Yun-Chun

2016-01-01

The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

Psychosocial Pathways to Sexually Transmitted Infection (STI) Risk Among Youth Transitioning Out of Foster Care: Evidence from a Longitudinal Cohort Study

PubMed Central

McCarty, Cari; Simoni, Jane; Dworsky, Amy; Courtney, Mark E.

2013-01-01

Purpose To test the fit of a theoretically driven conceptual model of pathways to STI risk among foster youth transitioning to adulthood. The model included: 1) historical abuse and foster care experiences, 2) mental health and attachment style in late adolescence, and 3) STI risk in young adulthood. Methods We used path analysis to analyze data from a longitudinal study of 732 youth transitioning out of foster care. Covariates included gender, race and an inverse probability weight. We also performed moderation analyses comparing models constrained and unconstrained by gender. Results Thirty percent reported they or a partner had been diagnosed with an STI. Probability of other measured STI risk behaviors ranged from 9% (having sex for money) to 79% (inconsistent condom use). Overall model fit was good (Standardized Root Mean Squared Residual of 0.026). Increased risk of oppositional/delinquent behaviors mediated an association between abuse history and STI risk, via increased inconsistent condom use. There was also a borderline association with having greater than 5 partners. Having a very close relationship with a caregiver and remaining in foster care beyond age 18 decreased STI risk. Moderation analysis revealed better model fit when coefficients were allowed to vary by gender versus a constrained model, but few significant differences in individual path coefficients were found between male and female-only models. Conclusions Interventions/policies that: 1) address externalizing trauma sequelae, 2) promote close, stable substitute caregiver relationships, and 3) extend care to age 21 years have the potential to decrease STI risk in this population. PMID:23859955

Tumor Necrosis Factor (TNF) –308G>A, Nitric Oxide Synthase 3 (NOS3) +894G>T Polymorphisms and Migraine Risk: A Meta-Analysis

PubMed Central

Chen, Min; Tang, Wenjing; Hou, Lei; Liu, Ruozhuo; Dong, Zhao; Han, Xun; Zhang, Xiaofei; Wan, Dongjun; Yu, Shengyuan

2015-01-01

Background and Objective Conflicting data have been reported on the association between tumor necrosis factor (TNF) –308G>A and nitric oxide synthase 3 (NOS3) +894G>T polymorphisms and migraine. We performed a meta-analysis of case-control studies to evaluate whether the TNF –308G>A and NOS3 +894G>T polymorphisms confer genetic susceptibility to migraine. Method We performed an updated meta-analysis for TNF –308G>A and a meta-analysis for NOS3 +894G>T based on studies published up to July 2014. We calculated study specific odds ratios (OR) and 95% confidence intervals (95% CI) assuming allele contrast, dominant model, recessive model, and co-dominant model as pooled effect estimates. Results Eleven studies in 6682 migraineurs and 22591 controls for TNF –308G>A and six studies in 1055 migraineurs and 877 controls for NOS3 +894G>T were included in the analysis. Neither indicated overall associations between gene polymorphisms and migraine risk. Subgroup analyses suggested that the “A” allele of the TNF –308G>A variant increases the risk of migraine among non-Caucasians (dominant model: pooled OR = 1.82; 95% CI 1.15 – 2.87). The risk of migraine with aura (MA) was increased among both Caucasians and non-Caucasians. Subgroup analyses suggested that the “T” allele of the NOS3 +894G>T variant increases the risk of migraine among non-Caucasians (co-dominant model: pooled OR = 2.10; 95% CI 1.14 – 3.88). Conclusions Our findings appear to support the hypothesis that the TNF –308G>A polymorphism may act as a genetic susceptibility factor for migraine among non-Caucasians and that the NOS3 +894G>T polymorphism may modulate the risk of migraine among non-Caucasians. PMID:26098763

A Simplified Approach to Risk Assessment Based on System Dynamics: An Industrial Case Study.

PubMed

Garbolino, Emmanuel; Chery, Jean-Pierre; Guarnieri, Franck

2016-01-01

Seveso plants are complex sociotechnical systems, which makes it appropriate to support any risk assessment with a model of the system. However, more often than not, this step is only partially addressed, simplified, or avoided in safety reports. At the same time, investigations have shown that the complexity of industrial systems is frequently a factor in accidents, due to interactions between their technical, human, and organizational dimensions. In order to handle both this complexity and changes in the system over time, this article proposes an original and simplified qualitative risk evaluation method based on the system dynamics theory developed by Forrester in the early 1960s. The methodology supports the development of a dynamic risk assessment framework dedicated to industrial activities. It consists of 10 complementary steps grouped into two main activities: system dynamics modeling of the sociotechnical system and risk analysis. This system dynamics risk analysis is applied to a case study of a chemical plant and provides a way to assess the technological and organizational components of safety. © 2016 Society for Risk Analysis.

Metabolic Syndrome Risk Profiles Among African American Adolescents

PubMed Central

Fitzpatrick, Stephanie L.; Lai, Betty S.; Brancati, Frederick L.; Golden, Sherita H.; Hill-Briggs, Felicia

2013-01-01

OBJECTIVE Although African American adolescents have the highest prevalence of obesity, they have the lowest prevalence of metabolic syndrome across all definitions used in previous research. To address this paradox, we sought to develop a model of the metabolic syndrome specific to African American adolescents. RESEARCH DESIGN AND METHODS Data from the National Health and Nutrition Examination Survey (2003–2010) of 822 nonpregnant, nondiabetic, African American adolescents (45% girls; aged 12 to 17 years) who underwent physical examinations and fasted at least 8 h were analyzed. We conducted a confirmatory factor analysis to model metabolic syndrome and then used latent profile analysis to identify metabolic syndrome risk groups among African American adolescents. We compared the risk groups on probability of prediabetes. RESULTS The best-fitting metabolic syndrome model consisted of waist circumference, fasting insulin, HDL, and systolic blood pressure. We identified three metabolic syndrome risk groups: low, moderate, and high risk (19% boys; 16% girls). Thirty-five percent of both boys and girls in the high-risk groups had prediabetes, a significantly higher prevalence compared with boys and girls in the low-risk groups. Among adolescents with BMI higher than the 85th percentile, 48 and 36% of boys and girls, respectively, were in the high-risk group. CONCLUSIONS Our findings provide a plausible model of the metabolic syndrome specific to African American adolescents. Based on this model, approximately 19 and 16% of African American boys and girls, respectively, are at high risk for having the metabolic syndrome. PMID:23093663

The Comprehensive Evaluation Method of Supervision Risk in Electricity Transaction Based on Unascertained Rational Number

NASA Astrophysics Data System (ADS)

Haining, Wang; Lei, Wang; Qian, Zhang; Zongqiang, Zheng; Hongyu, Zhou; Chuncheng, Gao

2018-03-01

For the uncertain problems in the comprehensive evaluation of supervision risk in electricity transaction, this paper uses the unidentified rational numbers to evaluation the supervision risk, to obtain the possible result and corresponding credibility of evaluation and realize the quantification of risk indexes. The model can draw the risk degree of various indexes, which makes it easier for the electricity transaction supervisors to identify the transaction risk and determine the risk level, assisting the decision-making and realizing the effective supervision of the risk. The results of the case analysis verify the effectiveness of the model.

Method and system for dynamic probabilistic risk assessment

NASA Technical Reports Server (NTRS)

Dugan, Joanne Bechta (Inventor); Xu, Hong (Inventor)

2013-01-01

The DEFT methodology, system and computer readable medium extends the applicability of the PRA (Probabilistic Risk Assessment) methodology to computer-based systems, by allowing DFT (Dynamic Fault Tree) nodes as pivot nodes in the Event Tree (ET) model. DEFT includes a mathematical model and solution algorithm, supports all common PRA analysis functions and cutsets. Additional capabilities enabled by the DFT include modularization, phased mission analysis, sequence dependencies, and imperfect coverage.

GEO Collisional Risk Assessment Based on Analysis of NASA-WISE Data and Modeling

DTIC Science & Technology

2015-10-18

GEO Collisional Risk Assessment Based on Analysis of NASA -WISE Data and Modeling Jeremy Murray Krezan1, Samantha Howard1, Phan D. Dao1, Derek...Surka2 1AFRL Space Vehicles Directorate,2Applied Technology Associates Incorporated From December 2009 through 2011 the NASA Wide-Field Infrared...of known debris. The NASA -WISE GEO belt debris population adds potentially thousands previously uncataloged objects. This paper describes

49 CFR Appendix D to Part 172 - Rail Risk Analysis Factors

Code of Federal Regulations, 2012 CFR

2012-10-01

... nature of the rail system, each carrier must select and document the analysis method/model used and identify the routes to be analyzed. D. The safety and security risk analysis must consider current data and... curvature; 7. Presence or absence of signals and train control systems along the route (“dark” versus...

Meta-analysis of the rs2075650 polymorphism and risk of Alzheimer disease.

PubMed

He, Ya; Li, Chen; Yang, Ying; Li, Yizhou; Wang, Yuan; Yang, Hua; Jin, Tianbo; Chen, Songsheng

2016-10-01

Several researchers have suggested that the rs2075650 polymorphism is significantly associated with an increased risk of developing Alzheimer disease (AD) in European. However, some others found inconsistent results in Asian (Chinese and Korean). We addressed the controversy through performing a meta-analysis of the relationship between rs2075650 in TOMM40 (translocase of outer mitochondrial membrane 40 homologue) and Alzheimer disease. We selected eight case-control studies involving 4290 cases of Alzheimer disease and 5556 healthy individuals. The association between the TOMM40 rs2075650 polymorphism and Alzheimer disease was examined by overall odds ratio (OR) with a 95 % confidence interval (CI). We used different genetic model analysis, sensitivity analysis, and assessments of bias in our meta-analysis. The pooled analysis showed the inconsistent results that TOMM40 rs2075650 polymorphism was associated with Alzheimer disease in European and Korean population in all genetic models, but there was no significant association between the TOMM40 rs2075650 polymorphism and Alzheimer disease risk in Chinese population. We conclude that rs2075650 in TOMM40 gene may increase the risk of Alzheimer disease.

Structural Health Monitoring Analysis for the Orbiter Wing Leading Edge

NASA Technical Reports Server (NTRS)

Yap, Keng C.

2010-01-01

This viewgraph presentation reviews Structural Health Monitoring Analysis for the Orbiter Wing Leading Edge. The Wing Leading Edge Impact Detection System (WLE IDS) and the Impact Analysis Process are also described to monitor WLE debris threats. The contents include: 1) Risk Management via SHM; 2) Hardware Overview; 3) Instrumentation; 4) Sensor Configuration; 5) Debris Hazard Monitoring; 6) Ascent Response Summary; 7) Response Signal; 8) Distribution of Flight Indications; 9) Probabilistic Risk Analysis (PRA); 10) Model Correlation; 11) Impact Tests; 12) Wing Leading Edge Modeling; 13) Ascent Debris PRA Results; and 14) MM/OD PRA Results.

Validation of the Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) Model.

PubMed

Binenbaum, Gil; Ying, Gui-Shuang; Tomlinson, Lauren A

2017-08-01

The Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) model uses birth weight (BW), gestational age at birth (GA), and weight gain rate to predict the risk of severe retinopathy of prematurity (ROP). In a model development study, it predicted all infants requiring treatment, while greatly reducing the number of examinations compared with current screening guidelines. To validate the CHOP ROP model in a multicenter cohort that is large enough to obtain a precise estimate of the model's sensitivity for treatment-requiring ROP. This investigation was a secondary analysis of data from the Postnatal Growth and Retinopathy of Prematurity (G-ROP) Study. The setting was 30 hospitals in the United States and Canada between January 1, 2006, and June 30, 2012. The dates of analysis were September 28 to October 5, 2015. Participants were premature infants at risk for ROP with a known ROP outcome. Sensitivity for Early Treatment of Retinopathy of Prematurity type 1 ROP and potential reduction in the number of infants requiring examinations. In the primary analysis, the CHOP ROP model was applied weekly to predict the risk of ROP. If the risk was above a cut-point level (high risk), examinations were indicated, while low-risk infants received no examinations. In a secondary analysis, low-risk infants received fewer examinations rather than no examinations. Participants included 7483 premature infants at risk for ROP with a known ROP outcome. Their median BW was 1070 g (range, 310-3000 g), and their median GA was 28 weeks (range, 22-35 weeks). Among them, 3575 (47.8%) were female, and their race/ethnicity was 3615 white (48.3%), 2310 black (30.9%), 233 Asian (3.1%), 93 Pacific Islander (1.2%), and 40 American Indian/Alaskan native (0.5%). The original CHOP ROP model correctly predicted 452 of 459 infants who developed type 1 ROP (sensitivity, 98.5%; 95% CI, 96.9%-99.3%), reducing the number of infants requiring examinations by 34.3% if only high-risk infants received examinations. Lowering the cut point to capture all type 1 ROP cases (sensitivity, 100%; 95% CI, 99.2%-100%) resulted in only 6.8% of infants not requiring examinations. However, if low-risk infants were examined at 37 weeks' postmenstrual age and followed up only if ROP was present at that examination, all type 1 ROP cases would be captured, and the number of examinations performed among infants with GA exceeding 27 weeks would be reduced by 28.4%. The CHOP ROP model demonstrated high but not 100% sensitivity and may be better used to reduce examination frequency. The model might be used reliably to guide a modified ROP screening schedule and decrease the number of examinations performed.

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.

Time Factor in the Theory of Anthropogenic Risk Prediction in Complex Dynamic Systems

NASA Astrophysics Data System (ADS)

Ostreikovsky, V. A.; Shevchenko, Ye N.; Yurkov, N. K.; Kochegarov, I. I.; Grishko, A. K.

2018-01-01

The article overviews the anthropogenic risk models that take into consideration the development of different factors in time that influence the complex system. Three classes of mathematical models have been analyzed for the use in assessing the anthropogenic risk of complex dynamic systems. These models take into consideration time factor in determining the prospect of safety change of critical systems. The originality of the study is in the analysis of five time postulates in the theory of anthropogenic risk and the safety of highly important objects. It has to be stressed that the given postulates are still rarely used in practical assessment of equipment service life of critically important systems. That is why, the results of study presented in the article can be used in safety engineering and analysis of critically important complex technical systems.

Climate change and the effects of dengue upon Australia: An analysis of health impacts and costs

NASA Astrophysics Data System (ADS)

Newth, D.; Gunasekera, D.

2010-08-01

Projected regional warming and climate change analysis and health impact studies suggest that Australia is potentially vulnerable to increased occurrence of vector borne diseases such as dengue fever. Expansion of the dengue fever host, Aedes aegypti could potentially pose a significant public health risk. To manage such health risks, there is a growing need to focus on adaptive risk management strategies. In this paper, we combine analyses from climate, biophysical and economic models with a high resolution population model for disease spread, the EpiCast model to analyse the health impacts and costs of spread of dengue fever. We demonstrate the applicability of EpiCast as a decision support tool to evaluate mitigation strategies to manage the public health risks associated with shifts in the distribution of dengue fever in Australia.

Abstracts and program proceedings of the 1994 meeting of the International Society for Ecological Modelling North American Chapter

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

Kercher, J.R.

1994-06-01

This document contains information about the 1994 meeting of the International Society for Ecological Modelling North American Chapter. The topics discussed include: extinction risk assessment modelling, ecological risk analysis of uranium mining, impacts of pesticides, demography, habitats, atmospheric deposition, and climate change.

Coffee consumption is not associated with ovarian cancer risk: a dose-response meta-analysis of prospective cohort studies.

PubMed

Berretta, Massimiliano; Micek, Agnieszka; Lafranconi, Alessandra; Rossetti, Sabrina; Di Francia, Raffaele; De Paoli, Paolo; Rossi, Paola; Facchini, Gaetano

2018-04-17

Coffee consumption has been associated with numerous cancers, but evidence on ovarian cancer risk is controversial. Therefore, we performed a meta-analysis on prospective cohort studies in order to review the evidence on coffee consumption and risk of ovarian cancer. Studies were identified through searching the PubMed and MEDLINE databases up to March 2017. Risk estimates were retrieved from the studies, and dose-response analysis was modelled by using restricted cubic splines. Additionally, a stratified analysis by menopausal status was performed. A total of 8 studies were eligible for the dose-response meta-analysis. Studies included in the analysis comprised 787,076 participants and 3,541 ovarian cancer cases. The results showed that coffee intake was not associated with ovarian cancer risk (RR = 1.06, 95% CI: 0.89, 1.26). Stratified and subgroup analysis showed consisted results. This comprehensive meta-analysis did not find evidence of an association between the consumption of coffee and risk of ovarian cancer.

Risk-Based Prioritization of Research for Aviation Security Using Logic-Evolved Decision Analysis

NASA Technical Reports Server (NTRS)

Eisenhawer, S. W.; Bott, T. F.; Sorokach, M. R.; Jones, F. P.; Foggia, J. R.

2004-01-01

The National Aeronautics and Space Administration is developing advanced technologies to reduce terrorist risk for the air transportation system. Decision support tools are needed to help allocate assets to the most promising research. An approach to rank ordering technologies (using logic-evolved decision analysis), with risk reduction as the metric, is presented. The development of a spanning set of scenarios using a logic-gate tree is described. Baseline risk for these scenarios is evaluated with an approximate reasoning model. Illustrative risk and risk reduction results are presented.

When Does Neoadjuvant Chemotherapy Really Avoid Radiotherapy? Clinical Predictors of Adjuvant Radiotherapy in Cervical Cancer.

PubMed

Papadia, Andrea; Bellati, Filippo; Bogani, Giorgio; Ditto, Antonino; Martinelli, Fabio; Lorusso, Domenica; Donfrancesco, Cristina; Gasparri, Maria Luisa; Raspagliesi, Francesco

2015-12-01

The aim of this study was to identify clinical variables that may predict the need for adjuvant radiotherapy after neoadjuvant chemotherapy (NACT) and radical surgery in locally advanced cervical cancer patients. A retrospective series of cervical cancer patients with International Federation of Gynecology and Obstetrics (FIGO) stages IB2-IIB treated with NACT followed by radical surgery was analyzed. Clinical predictors of persistence of intermediate- and/or high-risk factors at final pathological analysis were investigated. Statistical analysis was performed using univariate and multivariate analysis and using a model based on artificial intelligence known as artificial neuronal network (ANN) analysis. Overall, 101 patients were available for the analyses. Fifty-two (51 %) patients were considered at high risk secondary to parametrial, resection margin and/or lymph node involvement. When disease was confined to the cervix, four (4 %) patients were considered at intermediate risk. At univariate analysis, FIGO grade 3, stage IIB disease at diagnosis and the presence of enlarged nodes before NACT predicted the presence of intermediate- and/or high-risk factors at final pathological analysis. At multivariate analysis, only FIGO grade 3 and tumor diameter maintained statistical significance. The specificity of ANN models in evaluating predictive variables was slightly superior to conventional multivariable models. FIGO grade, stage, tumor diameter, and histology are associated with persistence of pathological intermediate- and/or high-risk factors after NACT and radical surgery. This information is useful in counseling patients at the time of treatment planning with regard to the probability of being subjected to pelvic radiotherapy after completion of the initially planned treatment.

Association between the BRCA2 rs144848 polymorphism and cancer susceptibility: a meta-analysis.

PubMed

Li, Qiuyan; Guan, Rongwei; Qiao, Yuandong; Liu, Chang; He, Ning; Zhang, Xuelong; Jia, Xueyuan; Sun, Haiming; Yu, Jingcui; Xu, Lidan

2017-06-13

The BRCA2 gene plays an important role in cancer carcinogenesis, and polymorphisms in this gene have been associated with cancer risk. The BRCA2 rs144848 polymorphism has been associated with several cancers, but results have been inconsistent. In the present study, a meta-analysis was performed to assess the association between the rs144848 polymorphism and cancer risk. Literature was searched from the databases of PubMed, Embase and Google Scholar before April 2016. The fixed or random effects model was used to calculate pooled odd ratios on the basis of heterogeneity. Meta-regression, sensitivity analysis, subgroup analysis and publication bias assessment were also performed using STATA 11.0 software according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009. A total of 40 relevant studies from 30 publications including 34,911 cases and 48,329 controls were included in the final meta-analysis. Among them, 22 studies focused on breast cancer, seven on ovarian cancer, five on non-Hodgkin lymphoma, and the remaining six studies examined various other cancers. The meta-analysis results showed that there were significant associations between the rs144848 polymorphism and cancer risk in all genetic models. Stratified by cancer type, the rs144848 polymorphism was associated with non-Hodgkin lymphoma. Stratified by study design, the allele model was associated with breast cancer risk in population-based studies. The meta-analysis suggests that the BRCA2 rs144848 polymorphism may play a role in cancer risk. Further well-designed studies are warranted to confirm these results.

An Agent-Based Model of Evolving Community Flood Risk.

PubMed

Tonn, Gina L; Guikema, Seth D

2018-06-01

Although individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent-based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near-miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high-risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in-depth behavioral and decision rules at the individual and community level. © 2017 Society for Risk Analysis.

Dependence in probabilistic modeling Dempster-Shafer theory and probability bounds analysis

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

Ferson, Scott; Nelsen, Roger B.; Hajagos, Janos

2015-05-01

This report summarizes methods to incorporate information (or lack of information) about inter-variable dependence into risk assessments that use Dempster-Shafer theory or probability bounds analysis to address epistemic and aleatory uncertainty. The report reviews techniques for simulating correlated variates for a given correlation measure and dependence model, computation of bounds on distribution functions under a specified dependence model, formulation of parametric and empirical dependence models, and bounding approaches that can be used when information about the intervariable dependence is incomplete. The report also reviews several of the most pervasive and dangerous myths among risk analysts about dependence in probabilistic models.

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation.

PubMed

Meertens, Linda J E; van Montfort, Pim; Scheepers, Hubertina C J; van Kuijk, Sander M J; Aardenburg, Robert; Langenveld, Josje; van Dooren, Ivo M A; Zwaan, Iris M; Spaanderman, Marc E A; Smits, Luc J M

2018-04-17

Prediction models may contribute to personalized risk-based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models. Prediction models based on routinely collected maternal parameters obtainable during first 16 weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web-based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation. Clinical value was evaluated by means of decision curve analysis and calculating classification accuracy for different risk thresholds. Four studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation, respectively. A subanalysis showed that the models discriminated poorly (AUC 0.51-0.56) for nulliparous women. Although we recalibrated the models, two models retained evidence of overfitting. The decision curve analysis showed low clinical benefit for the best performing models. This review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth. © 2018 The Authors Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).

Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China

NASA Astrophysics Data System (ADS)

Pei, Wei; Fu, Qiang; Liu, Dong; Li, Tian-xiao; Cheng, Kun; Cui, Song

2017-06-01

Droughts are natural disasters that pose significant threats to agricultural production as well as living conditions, and a spatial-temporal difference analysis of agricultural drought risk can help determine the spatial distribution and temporal variation of the drought risk within a region. Moreover, this type of analysis can provide a theoretical basis for the identification, prevention, and mitigation of drought disasters. In this study, the overall dispersion and local aggregation of projection points were based on research by Friedman and Tukey (IEEE Trans on Computer 23:881-890, 1974). In this work, high-dimensional samples were clustered by cluster analysis. The clustering results were represented by the clustering matrix, which determined the local density in the projection index. This method avoids the problem of determining a cutoff radius. An improved projection pursuit model is proposed that combines cluster analysis and the projection pursuit model, which offer advantages for classification and assessment, respectively. The improved model was applied to analyze the agricultural drought risk of 13 cities in Heilongjiang Province over 6 years (2004, 2006, 2008, 2010, 2012, and 2014). The risk of an agricultural drought disaster was characterized by 14 indicators and the following four aspects: hazard, exposure, sensitivity, and resistance capacity. The spatial distribution and temporal variation characteristics of the agricultural drought risk in Heilongjiang Province were analyzed. The spatial distribution results indicated that Suihua, Qigihar, Daqing, Harbin, and Jiamusi are located in high-risk areas, Daxing'anling and Yichun are located in low-risk areas, and the differences among the regions were primarily caused by the aspects exposure and resistance capacity. The temporal variation results indicated that the risk of agricultural drought in most areas presented an initially increasing and then decreasing trend. A higher value for the exposure aspect increased the risk of drought, whereas a higher value for the resistance capacity aspect reduced the risk of drought. Over the long term, the exposure level of the region presented limited increases, whereas the resistance capacity presented considerable increases. Therefore, the risk of agricultural drought in Heilongjiang Province will continue to exhibit a decreasing trend.

Human Reliability Analysis in Support of Risk Assessment for Positive Train Control

DOT National Transportation Integrated Search

2003-06-01

This report describes an approach to evaluating the reliability of human actions that are modeled in a probabilistic risk assessment : (PRA) of train control operations. This approach to human reliability analysis (HRA) has been applied in the case o...

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

PubMed

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

2016-06-01

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

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

NASA Astrophysics Data System (ADS)

Fontenot, Jonas David

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

Serine/threonine kinase 15 gene polymorphism and risk of digestive system cancers: A meta-analysis.

PubMed

Luo, Jianfei; Yan, Ruicheng; Zou, Li

2015-01-01

Previous studies have reported an association between the two coding polymorphisms (91T>A and 169G>A) of the serine/threonine kinase 15 (STK15) gene and the risk of digestive system cancers; however, the results are inconsistent. In the present study, a meta-analysis was carried out to assess the association between the two STK15 polymorphisms and the risk of digestive system cancers. Relevant studies were identified using PubMed, Web of Science, China National Knowledge Infrastructure, WanFang and VIP databases up to February 18, 2014. The pooled odds ratio (OR) with a 95% confidence interval (CI) was calculated using the fixed or random effects model. A total of 15 case-control studies from 14 publications were included. Of these, 15 studies concerned the 91T>A polymorphism and included 7,619 cases and 7,196 controls and four studies concerned the 161G>A polymorphism and included 826 cases and 713 controls. A significantly increased risk of digestive system cancers was observed for the 91T>A polymorphism (recessive model: OR, 1.19; 95% CI, 1.07-1.31). In subgroup analysis by ethnicity, a significant association was detected in Asian populations (recessive model: OR, 1.21; 95% CI, 1.08-1.36) but not in Caucasian and mixed populations. Stratification by tumor type indicated that the 91T>A polymorphism was associated with an increased risk of esophageal and colorectal cancers under the recessive model (OR, 1.19; 95% CI, 1.03-1.38; and OR, 1.24; 95% CI, 1.04-1.46; respectively); however, no significant association was observed between the 169G>A polymorphism and the risk of digestive system cancers in any of the genetic models. Furthermore, in subgroup analysis by ethnicity, similar results were observed in the Asian and Caucasian populations. The present meta-analysis demonstrated that the STK15 gene 91T>A polymorphism, but not the 169G>A polymorphism, may be a risk factor for digestive system cancers, particularly for esophageal and colorectal cancers.

Risk Decision Making Model for Reservoir Floodwater resources Utilization

NASA Astrophysics Data System (ADS)

Huang, X.

2017-12-01

Floodwater resources utilization(FRU) can alleviate the shortage of water resources, but there are risks. In order to safely and efficiently utilize the floodwater resources, it is necessary to study the risk of reservoir FRU. In this paper, the risk rate of exceeding the design flood water level and the risk rate of exceeding safety discharge are estimated. Based on the principle of the minimum risk and the maximum benefit of FRU, a multi-objective risk decision making model for FRU is constructed. Probability theory and mathematical statistics method is selected to calculate the risk rate; C-D production function method and emergy analysis method is selected to calculate the risk benefit; the risk loss is related to flood inundation area and unit area loss; the multi-objective decision making problem of the model is solved by the constraint method. Taking the Shilianghe reservoir in Jiangsu Province as an example, the optimal equilibrium solution of FRU of the Shilianghe reservoir is found by using the risk decision making model, and the validity and applicability of the model are verified.

External Validation of the Garvan Nomograms for Predicting Absolute Fracture Risk: The Tromsø Study

PubMed Central

Ahmed, Luai A.; Nguyen, Nguyen D.; Bjørnerem, Åshild; Joakimsen, Ragnar M.; Jørgensen, Lone; Størmer, Jan; Bliuc, Dana; Center, Jacqueline R.; Eisman, John A.; Nguyen, Tuan V.; Emaus, Nina

2014-01-01

Background Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort. Methods The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance. Results The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture. Conclusions The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction. PMID:25255221

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

A dynamical systems model for nuclear power plant risk

NASA Astrophysics Data System (ADS)

Hess, Stephen Michael

The recent transition to an open access generation marketplace has forced nuclear plant operators to become much more cost conscious and focused on plant performance. Coincidentally, the regulatory perspective also is in a state of transition from a command and control framework to one that is risk-informed and performance-based. Due to these structural changes in the economics and regulatory system associated with commercial nuclear power plant operation, there is an increased need for plant management to explicitly manage nuclear safety risk. Application of probabilistic risk assessment techniques to model plant hardware has provided a significant contribution to understanding the potential initiating events and equipment failures that can lead to core damage accidents. Application of the lessons learned from these analyses has supported improved plant operation and safety over the previous decade. However, this analytical approach has not been nearly as successful in addressing the impact of plant processes and management effectiveness on the risks of plant operation. Thus, the research described in this dissertation presents a different approach to address this issue. Here we propose a dynamical model that describes the interaction of important plant processes among themselves and their overall impact on nuclear safety risk. We first provide a review of the techniques that are applied in a conventional probabilistic risk assessment of commercially operating nuclear power plants and summarize the typical results obtained. The limitations of the conventional approach and the status of research previously performed to address these limitations also are presented. Next, we present the case for the application of an alternative approach using dynamical systems theory. This includes a discussion of previous applications of dynamical models to study other important socio-economic issues. Next, we review the analytical techniques that are applicable to analysis of these models. Details of the development of the mathematical risk model are presented. This includes discussion of the processes included in the model and the identification of significant interprocess interactions. This is followed by analysis of the model that demonstrates that its dynamical evolution displays characteristics that have been observed at commercially operating plants. The model is analyzed using the previously described techniques from dynamical systems theory. From this analysis, several significant insights are obtained with respect to the effective control of nuclear safety risk. Finally, we present conclusions and recommendations for further research.

Advanced Mechanistic 3D Spatial Modeling and Analysis Methods to Accurately Represent Nuclear Facility External Event Scenarios

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

Sezen, Halil; Aldemir, Tunc; Denning, R.

Probabilistic risk assessment of nuclear power plants initially focused on events initiated by internal faults at the plant, rather than external hazards including earthquakes and flooding. Although the importance of external hazards risk analysis is now well recognized, the methods for analyzing low probability external hazards rely heavily on subjective judgment of specialists, often resulting in substantial conservatism. This research developed a framework to integrate the risk of seismic and flooding events using realistic structural models and simulation of response of nuclear structures. The results of four application case studies are presented.

[Use of bivariate survival curves for analyzing mortality of heart failure and sudden death in dilated cardiomiopathy].

PubMed

Gregori, Dario; Rosato, Rosalba; Zecchin, Massimo; Di Lenarda, Andrea

2005-01-01

This paper discusses the use of bivariate survival curves estimators within the competing risk framework. Competing risks models are used for the analysis of medical data with more than one cause of death. The case of dilated cardiomiopathy is explored. Bivariate survival curves plot the conjoint mortality processes. The different graphic representation of bivariate survival analysis is the major contribute of this methodology to the competing risks analysis.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

PubMed Central

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-01-01

Background: In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods: As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. Results: The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions: The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities. PMID:27174328

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

PubMed

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-05-20

In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.

PubMed

Yokota, Shinichiroh; Ohe, Kazuhiko

2016-04-01

To construct and evaluate an easy-to-use fall risk prediction model based on the daily condition of inpatients from secondary use electronic medical record system data. The present authors scrutinized electronic medical record system data and created a dataset for analysis by including inpatient fall report data and Intensity of Nursing Care Needs data. The authors divided the analysis dataset into training data and testing data, then constructed the fall risk prediction model FiND from the training data, and tested the model using the testing data. The dataset for analysis contained 1,230,604 records from 46,241 patients. The sensitivity of the model constructed from the training data was 71.3% and the specificity was 66.0%. The verification result from the testing dataset was almost equivalent to the theoretical value. Although the model's accuracy did not surpass that of models developed in previous research, the authors believe FiND will be useful in medical institutions all over Japan because it is composed of few variables (only age, sex, and the Intensity of Nursing Care Needs items), and the accuracy for unknown data was clear. © 2016 Japan Academy of Nursing Science.

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

PubMed

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

2012-01-01

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

Mediation of late adolescent health-risk behaviors and gender influences.

PubMed

Christopherson, Toni Michelle; Conner, Bradley T

2012-11-01

This study explored how multiple bioecological constructs operate to explain health-risk behaviors in late adolescence and to test for moderator effects of gender. This was a descriptive, cross-sectional study with a convenience sample of 437 predominately Caucasian late adolescents with an average age of 19 years who lived in Northern California. Parental Attachment, Shyness, Loneliness, Law Abidance, and Youth Risk Behaviors were measured with self-report tools and analyzed using structural equation modeling. Confirmatory factor analysis indicated that the data fit the model well. Analysis of group differences revealed that gender moderated the relationships among the measured variables; thus, data were analyzed in independent gender-based models. Structural modeling demonstrated good model fit for each gender. Shyness and parental attachment each were associated with loneliness. Loneliness was associated with smoking. Loneliness linked the relationship between shyness, parental attachment, and smoking. Parental attachment was associated with law abidance. Law abidance was associated with sexual behaviors for female adolescents only. This study provides valuable insights for public health nurses as it pertains to late adolescent health-risk behaviors. Nurses should use screening tools and techniques to ensure appropriate referrals and interventions to meet the needs of at-risk adolescents. © 2012 Wiley Periodicals, Inc.

Consumption of Yogurt and the Incident Risk of Cardiovascular Disease: A Meta-Analysis of Nine Cohort Studies.

PubMed

Wu, Lei; Sun, Dali

2017-03-22

Previous systematic reviews and meta-analyses have evaluated the association of dairy consumption and the risk of cardiovascular disease (CVD). However, the findings were inconsistent. No quantitative analysis has specifically assessed the effect of yogurt intake on the incident risk of CVD. We searched the PubMed and the Embase databases from inception to 10 January 2017. A generic inverse-variance method was used to pool the fully-adjusted relative risks (RRs) and the corresponding 95% confidence intervals (CIs) with a random-effects model. A generalized least squares trend estimation model was used to calculate the specific slopes in the dose-response analysis. The present systematic review and meta-analysis identified nine prospective cohort articles involving a total of 291,236 participants. Compared with the lowest category, highest category of yogurt consumption was not significantly related with the incident risk of CVD, and the RR (95% CI) was 1.01 (0.95, 1.08) with an evidence of significant heterogeneity (I² = 52%). However, intake of ≥200 g/day yogurt was significantly associated with a lower risk of CVD in the subgroup analysis. There was a trend that a higher level of yogurt consumption was associated with a lower incident risk of CVD in the dose-response analysis. A daily dose of ≥200 g yogurt intake might be associated with a lower incident risk of CVD. Further cohort studies and randomized controlled trials are still demanded to establish and confirm the observed association in populations with different characteristics.

Consumption of Yogurt and the Incident Risk of Cardiovascular Disease: A Meta-Analysis of Nine Cohort Studies

PubMed Central

Wu, Lei; Sun, Dali

2017-01-01

Previous systematic reviews and meta-analyses have evaluated the association of dairy consumption and the risk of cardiovascular disease (CVD). However, the findings were inconsistent. No quantitative analysis has specifically assessed the effect of yogurt intake on the incident risk of CVD. We searched the PubMed and the Embase databases from inception to 10 January 2017. A generic inverse-variance method was used to pool the fully-adjusted relative risks (RRs) and the corresponding 95% confidence intervals (CIs) with a random-effects model. A generalized least squares trend estimation model was used to calculate the specific slopes in the dose-response analysis. The present systematic review and meta-analysis identified nine prospective cohort articles involving a total of 291,236 participants. Compared with the lowest category, highest category of yogurt consumption was not significantly related with the incident risk of CVD, and the RR (95% CI) was 1.01 (0.95, 1.08) with an evidence of significant heterogeneity (I2 = 52%). However, intake of ≥200 g/day yogurt was significantly associated with a lower risk of CVD in the subgroup analysis. There was a trend that a higher level of yogurt consumption was associated with a lower incident risk of CVD in the dose-response analysis. A daily dose of ≥200 g yogurt intake might be associated with a lower incident risk of CVD. Further cohort studies and randomized controlled trials are still demanded to establish and confirm the observed association in populations with different characteristics. PMID:28327514

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.

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.

Non-Spatial Analysis of Relative Risk of Dengue Disease in Bandung Using Poisson-gamma and Log-normal Models: A Case Study of Dengue Data from Santo Borromeus Hospital in 2013

NASA Astrophysics Data System (ADS)

Irawan, R.; Yong, B.; Kristiani, F.

2017-02-01

Bandung, one of the cities in Indonesia, is vulnerable to dengue disease for both early-stage (Dengue Fever) and severe-stage (Dengue Haemorrhagic Fever and Dengue Shock Syndrome). In 2013, there were 5,749 patients in Bandung and 2,032 of the patients were hospitalized in Santo Borromeus Hospital. In this paper, there are two models, Poisson-gamma and Log-normal models, that use Bayesian inference to estimate the value of the relative risk. The calculation is done by Markov Chain Monte Carlo method which is the simulation using Gibbs Sampling algorithm in WinBUGS 1.4.3 software. The analysis results for dengue disease of 30 sub-districts in Bandung in 2013 based on Santo Borromeus Hospital’s data are Coblong and Bandung Wetan sub-districts had the highest relative risk using both models for the early-stage, severe-stage, and all stages. Meanwhile, Cinambo sub-district had the lowest relative risk using both models for the severe-stage and all stages and BojongloaKaler sub-district had the lowest relative risk using both models for the early-stage. For the model comparison using DIC (Deviance Information Criterion) method, the Log-normal model is a better model for the early-stage and severe-stage, but for the all stages, the Poisson-gamma model is a better model which fits the data.

Meta-analysis of the association of MTHFR polymorphisms with multiple myeloma risk

PubMed Central

Ma, Li-Min; Ruan, Lin-Hai; Yang, Hai-Ping

2015-01-01

The association of methylenetetrahydrofolate reductase (MTHFR) polymorphisms with multiple myeloma (MM) risk has been explored, but the results remain controversial. Thus, a meta-analysis was performed to provide a comprehensively estimate. The case-control studies about MTHFR C677T and A1298C polymorphisms with MM risk were collected by searching PubMed, Elsevier, China National Knowledge Infrastructure and Wanfang Databases. Odds ratios (ORs) with 95% confidence intervals (CIs) were applied to assess the strength of association. Overall, no significant association was found between MTHFR A1298C polymorphism and MM risk under all four genetic models (AC vs. AA, OR = 0.99, 95%CI = 0.82-1.20; CC vs. AA, OR = 1.14, 95%CI = 0.77-1.68; recessive model, OR = 1.10, 95%CI = 0.76-1.59; dominant model, OR = 1.01, 95%CI = 0.84-1.22). The risk was also not significantly altered for C677T polymorphism and MM in overall comparisons (CT vs. CC, OR = 1.04, 95%CI = 0.93-1.17; TT vs. CC, OR = 1.16, 95%CI = 0.98-1.37; recessive model, OR = 1.13, 95%CI = 0.98-1.32; dominant model, OR = 1.07, 95%CI = 0.96-1.20). In subgroup analyses by ethnicity, no significant association was observed in both Caucasians and Asians. This meta-analysis suggested that MTHFR polymorphisms were not associated with MM risk. PMID:26022785

Spatial modeling of cutaneous leishmaniasis in the Andean region of Colombia.

PubMed

Pérez-Flórez, Mauricio; Ocampo, Clara Beatriz; Valderrama-Ardila, Carlos; Alexander, Neal

2016-06-27

The objective of this research was to identify environmental risk factors for cutaneous leishmaniasis (CL) in Colombia and map high-risk municipalities. The study area was the Colombian Andean region, comprising 715 rural and urban municipalities. We used 10 years of CL surveillance: 2000-2009. We used spatial-temporal analysis - conditional autoregressive Poisson random effects modelling - in a Bayesian framework to model the dependence of municipality-level incidence on land use, climate, elevation and population density. Bivariable spatial analysis identified rainforests, forests and secondary vegetation, temperature, and annual precipitation as positively associated with CL incidence. By contrast, livestock agroecosystems and temperature seasonality were negatively associated. Multivariable analysis identified land use - rainforests and agro-livestock - and climate - temperature, rainfall and temperature seasonality - as best predictors of CL. We conclude that climate and land use can be used to identify areas at high risk of CL and that this approach is potentially applicable elsewhere in Latin America.

Polymorphisms in Telomere Length Associated TERC and TERT predispose for Ischemic Stroke in a Chinese Han population.

PubMed

Zhang, Shuo; Ji, Guofa; Liang, Yiqian; Zhang, Rui; Shi, Puyu; Guo, Dangshe; Li, Chunqi; Feng, Jing; Liu, Feng; Peng, Rong; Chen, Mingwei

2017-01-06

The role of telomere in genomic stability is an established fact. Variation in leukocyte telomere length (LTL) has been considered a crucial factor that associated with age-associated diseases. To elucidate the association between LTL variation and ischemic stroke (IS) risk, we selected ten single nucleotide polymorphisms (SNPs) in three genes (TERC, TERT and RTEL1) that previously reported link to LTL, and genotyped SNPs of these genes in a case-control study. The association between polymorphisms and IS risk were tested by Chi squared test and haplotype analysis. In allele association analysis, allele "C" in rs10936599 of TERC gene and allele "G" in rs2853677 of TERT gene were found to have an increased risk of IS when compared with allele "T" and "A", respectively. Model association analysis showed that genotype "G/A" in the overdominant model and genotypes "G/A" and "A/A" in the dominant model of rs2242652 presented a more likelihood to have IS. Another TERT locus (rs2853677) with genotype "G" was also found IS-related risky in the log-additive model. Taken together, our results suggest a potential association between LTL related TERC, TERT gene variants and ischemic stroke risk.

Evaluating Determinants of Environmental Risk Perception for Risk Management in Contaminated Sites

PubMed Central

Janmaimool, Piyapong; Watanabe, Tsunemi

2014-01-01

Understanding the differences in the risk judgments of residents of industrial communities potentially provides insights into how to develop appropriate risk communication strategies. This study aimed to explore citizens’ fundamental understanding of risk-related judgments and to identify the factors contributing to perceived risks. An exploratory model was created to investigate the public’s risk judgments. In this model, the relationship between laypeople’s perceived risks and the factors related to the physical nature of risks (such as perceived probability of environmental contamination, probability of receiving impacts, and severity of catastrophic consequences) were examined by means of multiple regression analysis. Psychological factors, such as the ability to control the risks, concerns, experiences, and perceived benefits of industrial development were also included in the analysis. The Maptaphut industrial area in Rayong Province, Thailand was selected as a case study. A survey of 181 residents of communities experiencing different levels of hazardous gas contamination revealed rational risk judgments by inhabitants of high-risk and moderate-risk communities, based on their perceived probability of contamination, probability of receiving impacts, and perceived catastrophic consequences. However, risks assessed by people in low-risk communities could not be rationally explained and were influenced by their collective experiences. PMID:24937530

Probabilistic risk analysis and terrorism risk.

PubMed

Ezell, Barry Charles; Bennett, Steven P; von Winterfeldt, Detlof; Sokolowski, John; Collins, Andrew J

2010-04-01

Since the terrorist attacks of September 11, 2001, and the subsequent establishment of the U.S. Department of Homeland Security (DHS), considerable efforts have been made to estimate the risks of terrorism and the cost effectiveness of security policies to reduce these risks. DHS, industry, and the academic risk analysis communities have all invested heavily in the development of tools and approaches that can assist decisionmakers in effectively allocating limited resources across the vast array of potential investments that could mitigate risks from terrorism and other threats to the homeland. Decisionmakers demand models, analyses, and decision support that are useful for this task and based on the state of the art. Since terrorism risk analysis is new, no single method is likely to meet this challenge. In this article we explore a number of existing and potential approaches for terrorism risk analysis, focusing particularly on recent discussions regarding the applicability of probabilistic and decision analytic approaches to bioterrorism risks and the Bioterrorism Risk Assessment methodology used by the DHS and criticized by the National Academies and others.

Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data.

PubMed

Gim, Jungsoo; Kim, Wonji; Kwak, Soo Heon; Choi, Hosik; Park, Changyi; Park, Kyong Soo; Kwon, Sunghoon; Park, Taesung; Won, Sungho

2017-11-01

Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance. Copyright © 2017 by the Genetics Society of America.

Genetic polymorphisms in the IL-18 gene and ulcerative colitis risk: a meta-analysis.

PubMed

Wang, Ying; Tong, Jing; Chang, Bing; Wang, Bai-Fang; Zhang, Dai; Wang, Bing-Yuan

2014-07-01

This meta-analysis was performed to evaluate the relationships between genetic polymorphisms in the IL-18 gene and ulcerative colitis (UC) risk. The PubMed, CISCOM, CINAHL, Web of Science, Google Scholar, EBSCO, Cochrane Library, and CBM databases were searched for relevant articles published before November 1st, 2013 without any language restrictions. Meta-analysis was conducted using the STATA 12.0 software. Crude odds ratios (ORs) with their 95% confidence intervals (95% CI) were calculated. Eight case-control studies with a total of 1000 UC cases and 1392 healthy subjects met the inclusion criteria. Six common polymorphisms in the IL-18 gene were evaluated, including rs1946518 A>C, rs187238 G>C, rs917997 G>A, Codon35, rs1946519 C>A, and rs360718 A>C. The results of our meta-analysis suggest that the IL-18 rs1946518 (allele model: OR=1.22, 95% CI: 1.01-1.48, p=0.039; dominant model: OR=1.44, 95% CI: 1.01-2.06, p=0.045; respectively), rs187238 (allele model: OR=1.38, 95% CI: 1.19-1.61, p<0.001; dominant model: OR=1.50, 95% CI: 1.03-2.19, p=0.034; respectively), and rs360718 (allele model: OR=2.18, 95% CI: 1.22-3.90, p=0.008) polymorphisms might be strongly correlated with an increased risk of UC. A subgroup analysis was conducted to investigate the effect of ethnicity on an individual's risk of UC. Our results revealed positive significant correlations between IL-18 genetic polymorphisms and an increased risk of UC among Asians (allele model: OR=1.36, 95% CI: 1.16-1.60, p<0.001; dominant model: OR=1.50, 95% CI: 1.14-1.98, p=0.004; respectively) and Africans (allele model: OR=1.45, 95% CI: 1.03-2.05, p=0.034), but not among Caucasians (all p>0.05). Our findings provide convincing evidence that IL-18 genetic polymorphisms may contribute to susceptibility to UC, especially the rs1946518, rs187238, and rs360718 polymorphisms among Asians and Africans.

Implementing the Keele stratified care model for patients with low back pain: an observational impact study.

PubMed

Bamford, Adrian; Nation, Andy; Durrell, Susie; Andronis, Lazaros; Rule, Ellen; McLeod, Hugh

2017-02-03

The Keele stratified care model for management of low back pain comprises use of the prognostic STarT Back Screening Tool to allocate patients into one of three risk-defined categories leading to associated risk-specific treatment pathways, such that high-risk patients receive enhanced treatment and more sessions than medium- and low-risk patients. The Keele model is associated with economic benefits and is being widely implemented. The objective was to assess the use of the stratified model following its introduction in an acute hospital physiotherapy department setting in Gloucestershire, England. Physiotherapists recorded data on 201 patients treated using the Keele model in two audits in 2013 and 2014. To assess whether implementation of the stratified model was associated with the anticipated range of treatment sessions, regression analysis of the audit data was used to determine whether high- or medium-risk patients received significantly more treatment sessions than low-risk patients. The analysis controlled for patient characteristics, year, physiotherapists' seniority and physiotherapist. To assess the physiotherapists' views on the usefulness of the stratified model, audit data on this were analysed using framework methods. To assess the potential economic consequences of introducing the stratified care model in Gloucestershire, published economic evaluation findings on back-related National Health Service (NHS) costs, quality-adjusted life years (QALYs) and societal productivity losses were applied to audit data on the proportion of patients by risk classification and estimates of local incidence. When the Keele model was implemented, patients received significantly more treatment sessions as the risk-rating increased, in line with the anticipated impact of targeted treatment pathways. Physiotherapists were largely positive about using the model. The potential annual impact of rolling out the model across Gloucestershire is a gain in approximately 30 QALYs, a reduction in productivity losses valued at £1.4 million and almost no change to NHS costs. The Keele model was implemented and risk-specific treatment pathways successfully used for patients presenting with low back pain. Applying published economic evidence to the Gloucestershire locality suggests that substantial health and productivity outcomes would be associated with rollout of the Keele model while being cost-neutral for the NHS.

A time series modeling approach in risk appraisal of violent and sexual recidivism.

PubMed

Bani-Yaghoub, Majid; Fedoroff, J Paul; Curry, Susan; Amundsen, David E

2010-10-01

For over half a century, various clinical and actuarial methods have been employed to assess the likelihood of violent recidivism. Yet there is a need for new methods that can improve the accuracy of recidivism predictions. This study proposes a new time series modeling approach that generates high levels of predictive accuracy over short and long periods of time. The proposed approach outperformed two widely used actuarial instruments (i.e., the Violence Risk Appraisal Guide and the Sex Offender Risk Appraisal Guide). Furthermore, analysis of temporal risk variations based on specific time series models can add valuable information into risk assessment and management of violent offenders.

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning

PubMed Central

2018-01-01

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray, gene coexpression network analysis was performed to identify survival-related networks. A deep learning based risk stratification model was constructed with representative genes of these networks. The model was validated in two test sets. Survival analysis was performed using the output of the model to evaluate whether it could predict patients' survival independent of clinicopathological variables. Five networks were significantly associated with patients' survival. Considering prognostic significance and representativeness, genes of the two survival-related networks were selected for input of the model. The output of the model was significantly associated with patients' survival in two test sets and training set (p < 0.00001, p < 0.0001 and p = 0.02 for training and test sets 1 and 2, resp.). In multivariate analyses, the model was associated with patients' prognosis independent of other clinicopathological features. Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature and clinical application of deep learning in genomic data science for prognosis prediction. PMID:29581968

A probabilistic topic model for clinical risk stratification from electronic health records.

PubMed

Huang, Zhengxing; Dong, Wei; Duan, Huilong

2015-12-01

Risk stratification aims to provide physicians with the accurate assessment of a patient's clinical risk such that an individualized prevention or management strategy can be developed and delivered. Existing risk stratification techniques mainly focus on predicting the overall risk of an individual patient in a supervised manner, and, at the cohort level, often offer little insight beyond a flat score-based segmentation from the labeled clinical dataset. To this end, in this paper, we propose a new approach for risk stratification by exploring a large volume of electronic health records (EHRs) in an unsupervised fashion. Along this line, this paper proposes a novel probabilistic topic modeling framework called probabilistic risk stratification model (PRSM) based on Latent Dirichlet Allocation (LDA). The proposed PRSM recognizes a patient clinical state as a probabilistic combination of latent sub-profiles, and generates sub-profile-specific risk tiers of patients from their EHRs in a fully unsupervised fashion. The achieved stratification results can be easily recognized as high-, medium- and low-risk, respectively. In addition, we present an extension of PRSM, called weakly supervised PRSM (WS-PRSM) by incorporating minimum prior information into the model, in order to improve the risk stratification accuracy, and to make our models highly portable to risk stratification tasks of various diseases. We verify the effectiveness of the proposed approach on a clinical dataset containing 3463 coronary heart disease (CHD) patient instances. Both PRSM and WS-PRSM were compared with two established supervised risk stratification algorithms, i.e., logistic regression and support vector machine, and showed the effectiveness of our models in risk stratification of CHD in terms of the Area Under the receiver operating characteristic Curve (AUC) analysis. As well, in comparison with PRSM, WS-PRSM has over 2% performance gain, on the experimental dataset, demonstrating that incorporating risk scoring knowledge as prior information can improve the performance in risk stratification. Experimental results reveal that our models achieve competitive performance in risk stratification in comparison with existing supervised approaches. In addition, the unsupervised nature of our models makes them highly portable to the risk stratification tasks of various diseases. Moreover, patient sub-profiles and sub-profile-specific risk tiers generated by our models are coherent and informative, and provide significant potential to be explored for the further tasks, such as patient cohort analysis. We hypothesize that the proposed framework can readily meet the demand for risk stratification from a large volume of EHRs in an open-ended fashion. Copyright © 2015 Elsevier Inc. All rights reserved.

Information Presentation in Decision and Risk Analysis: Answered, Partly Answered, and Unanswered Questions.

PubMed

Keller, L Robin; Wang, Yitong

2017-06-01

For the last 30 years, researchers in risk analysis, decision analysis, and economics have consistently proven that decisionmakers employ different processes for evaluating and combining anticipated and actual losses, gains, delays, and surprises. Although rational models generally prescribe a consistent response, people's heuristic processes will sometimes lead them to be inconsistent in the way they respond to information presented in theoretically equivalent ways. We point out several promising future research directions by listing and detailing a series of answered, partly answered, and unanswered questions. © 2016 Society for Risk Analysis.

Globally-Applicable Predictive Wildfire Model   a Temporal-Spatial GIS Based Risk Analysis Using Data Driven Fuzzy Logic Functions

NASA Astrophysics Data System (ADS)

van den Dool, G.

2017-11-01

This study (van den Dool, 2017) is a proof of concept for a global predictive wildfire model, in which the temporal-spatial characteristics of wildfires are placed in a Geographical Information System (GIS), and the risk analysis is based on data-driven fuzzy logic functions. The data sources used in this model are available as global datasets, but subdivided into three pilot areas: North America (California/Nevada), Europe (Spain), and Asia (Mongolia), and are downscaled to the highest resolution (3-arc second). The GIS is constructed around three themes: topography, fuel availability and climate. From the topographical data, six derived sub-themes are created and converted to a fuzzy membership based on the catchment area statistics. The fuel availability score is a composite of four data layers: land cover, wood loads, biomass, biovolumes. As input for the climatological sub-model reanalysed daily averaged, weather-related data is used, which is accumulated to a global weekly time-window (to account for the uncertainty within the climatological model) and forms the temporal component of the model. The final product is a wildfire risk score (from 0 to 1) by week, representing the average wildfire risk in an area. To compute the potential wildfire risk the sub-models are combined usinga Multi-Criteria Approach, and the model results are validated against the area under the Receiver Operating Characteristic curve.

Are iso-osmolar, as compared to low-osmolar, contrast media cost-effective in patients undergoing cardiac catheterization? An economic analysis.

PubMed

Hiremath, Swapnil; Akbari, Ayub; Wells, George A; Chow, Benjamin J W

2018-04-23

Contrast-induced acute kidney injury is a prominent complication following cardiac catheterization, though the risk has progressively decreased in recent times with appropriate risk stratification and use of safer contrast agents. Despite data supporting further lowering of risk with the iso-osmolar agent, iodixanol, uptake has lagged, perhaps due to increased upfront cost of this agent. We undertook an economic analysis to estimate the cost-effectiveness of a strategy utilizing iodixanol compared to using a low-osmolar contrast agent. We created a Markov model to evaluate the two strategies, and included a differential relative risk of contrast-induced acute kidney injury, based on a systematic review of the literature. Downstream clinical events, including need for dialysis and mortality, were modeled using data from existing published literature. A third-party payer perspective was utilized for the analysis and presentation of the primary economic analysis. The strategy of using iodixanol dominated in both the low-risk and high-risk base case analyses. However, the difference was quite small in the low-risk scenario (lifetime cost: C$678,034 vs. C$678,059 and life expectancy: 19.80 vs. 19.72 years). The difference was more marked (life expectancy 15.65 vs. 14.15 years and cost C$680,989 vs. C$682,023) in the high-risk case analysis. This was robust across most of the variables tested in sensitivity analyses. The use of iodixanol, compared with low-osmolar contrast agents, for cardiac catheterization, results in a small benefit clinical outcomes, and in a savings in direct healthcare costs. Overall, our analysis supports the use of iodixanol for cardiac catheterization, especially in patients at high risk of acute kidney injury.

"Birds of a Feather" Fail Together: Exploring the Nature of Dependency in SME Defaults.

PubMed

Calabrese, Raffaella; Andreeva, Galina; Ansell, Jake

2017-08-11

This article studies the effects of incorporating the interdependence among London small business defaults into a risk analysis framework using the data just before the financial crisis. We propose an extension from standard scoring models to take into account the spatial dimensions and the demographic characteristics of small and medium-sized enterprises (SMEs), such as legal form, industry sector, and number of employees. We estimate spatial probit models using different distance matrices based only on the spatial location or on an interaction between spatial locations and demographic characteristics. We find that the interdependence or contagion component defined on spatial and demographic characteristics is significant and that it improves the ability to predict defaults of non-start-ups in London. Furthermore, including contagion effects among SMEs alters the parameter estimates of risk determinants. The approach can be extended to other risk analysis applications where spatial risk may incorporate correlation based on other aspects. © 2017 Society for Risk Analysis.

Benefit, risk and cost of new oral anticoagulants and warfarin in atrial fibrillation; A multicriteria decision analysis.

PubMed

Mendoza-Sanchez, Jose; Silva, Federico; Rangel, Lady; Jaramillo, Linda; Mendoza, Leidy; Garzon, Jenny; Quiroga, Andrea

2018-01-01

Warfarin and new oral anticoagulants are effective in reducing stroke in atrial fibrillation; however, the benefits and risks rates in clinical trials show heterogeneity for each anticoagulant, and is unknown the cost influence on a model considering most of the treatment consequences. We designed a benefit-risk and cost assessment of oral anticoagulants. We followed the roadmap proposed by IMI-PROTECT and the considerations of emerged good practice to perform Multi-Criteria Decision Analysis (MCDA). The roadmap defines the following steps: (1) planning, (2) evidence gathering and data preparation, (3) analyses, (4) explorations, and (5) conclusions. We defined two reference points (0-100) to allocate numerical values for scores and weights, and used an analogue numeric scale to assess physicians' preferences. As benefits of the anticoagulant therapy, we included reductions in stroke and all-cause mortality; intracranial haemorrhage, gastrointestinal haemorrhage, minor bleeding and myocardial infarction were considered risks. We also made an estimation of the annual drug cost per person. The scores were: Apixaban 33, Dabigatrán 25, warfarin 18 and Rivaroxaban 14 this score reveals the most preferred up to the less preferred option, considering the benefit-risk ratio and drug costs altogether. The relative model weights were: 51.1% for risks, 40.4% for benefits and 8.5% for cost. The sensitivity analysis confirms the model robustness. From this analysis, apixaban should be considered as the preferred anticoagulant option -due to a better benefit-risk balance and a minor cost influence- followed by dabigatran, warfarin and rivaroxaban.

Association between ADAM metallopeptidase domain 33 gene polymorphism and risk of childhood asthma: a meta-analysis.

PubMed

Sun, F J; Zou, L Y; Tong, D M; Lu, X Y; Li, J; Deng, C B

2017-08-31

This study aimed to investigate the association between ADAM metallopeptidase domain 33 (ADAM33) gene polymorphisms and the risk of childhood asthma. The relevant studies about the relationship between ADAM33 gene polymorphisms and childhood asthma were searched from electronic databases and the deadline of retrieval was May 2016. The single nucleotide polymorphisms (SNPs) of ADAM33 (rs511898, rs2280092, rs3918396, rs528557, rs2853209, rs44707, rs2280091 and rs2280089) were analyzed based on several models including the allele, codominant, recessive and dominant models. The results showed that the ADAM33 rs2280091 polymorphism in all four genetic models was associated with an increased risk of childhood asthma. Positive associations were also found between the polymorphisms rs2280090, rs2787094, rs44707 and rs528557 and childhood asthma in some genetic models. This meta-analysis suggested that ADAM33 polymorphisms rs2280091, rs2280090, rs2787094, rs44707 and rs528557 were significantly associated with a high risk of childhood asthma.

Psychosocial pathways to sexually transmitted infection risk among youth transitioning out of foster care: evidence from a longitudinal cohort study.

PubMed

Ahrens, Kym R; McCarty, Cari; Simoni, Jane; Dworsky, Amy; Courtney, Mark E

2013-10-01

To test the fit of a theoretically driven conceptual model of pathways to sexually transmitted infection (STI) risk among foster youth transitioning to adulthood. The model included (1) historical abuse and foster care experiences; (2) mental health and attachment style in late adolescence; and (3) STI risk in young adulthood. We used path analysis to analyze data from a longitudinal study of 732 youth transitioning out of foster care. Covariates included gender, race, and an inverse probability weight. We also performed moderation analyses comparing models constrained and unconstrained by gender. Thirty percent reported they or a partner had been diagnosed with an STI. Probability of other measured STI risk behaviors ranged from 9% (having sex for money) to 79% (inconsistent condom use). Overall model fit was good (Standardized Root Mean Square Residual of .026). Increased risk of oppositional/delinquent behaviors mediated an association between abuse history and STI risk, via increased inconsistent condom use. There was also a borderline association with having greater than five partners. Having a very close relationship with a caregiver and remaining in foster care beyond age 18 years decreased STI risk. Moderation analysis revealed better model fit when coefficients were allowed to vary by gender versus a constrained model, but few significant differences in individual path coefficients were found between male and female-only models. Interventions/policies that (1) address externalizing trauma sequelae; (2) promote close, stable substitute caregiver relationships; and (3) extend care to age 21 years have the potential to decrease STI risk in this population. Copyright © 2013 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Empirical Analysis of Farm Credit Risk under the Structure Model

ERIC Educational Resources Information Center

Yan, Yan

2009-01-01

The study measures farm credit risk by using farm records collected by Farm Business Farm Management (FBFM) during the period 1995-2004. The study addresses the following questions: (1) whether farm's financial position is fully described by the structure model, (2) what are the determinants of farm capital structure under the structure model, (3)…

Advanced uncertainty modelling for container port risk analysis.

PubMed

Alyami, Hani; Yang, Zaili; Riahi, Ramin; Bonsall, Stephen; Wang, Jin

2016-08-13

Globalization has led to a rapid increase of container movements in seaports. Risks in seaports need to be appropriately addressed to ensure economic wealth, operational efficiency, and personnel safety. As a result, the safety performance of a Container Terminal Operational System (CTOS) plays a growing role in improving the efficiency of international trade. This paper proposes a novel method to facilitate the application of Failure Mode and Effects Analysis (FMEA) in assessing the safety performance of CTOS. The new approach is developed through incorporating a Fuzzy Rule-Based Bayesian Network (FRBN) with Evidential Reasoning (ER) in a complementary manner. The former provides a realistic and flexible method to describe input failure information for risk estimates of individual hazardous events (HEs) at the bottom level of a risk analysis hierarchy. The latter is used to aggregate HEs safety estimates collectively, allowing dynamic risk-based decision support in CTOS from a systematic perspective. The novel feature of the proposed method, compared to those in traditional port risk analysis lies in a dynamic model capable of dealing with continually changing operational conditions in ports. More importantly, a new sensitivity analysis method is developed and carried out to rank the HEs by taking into account their specific risk estimations (locally) and their Risk Influence (RI) to a port's safety system (globally). Due to its generality, the new approach can be tailored for a wide range of applications in different safety and reliability engineering and management systems, particularly when real time risk ranking is required to measure, predict, and improve the associated system safety performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Study of the Factors Associated with Risk for Development of Pressure Ulcers: A Longitudinal Analysis

PubMed Central

Thomas, Elizebeth; Vinodkumar, Sudhaya; Mathew, Silvia; Setia, Maninder Singh

2015-01-01

Background: Pressure ulcers (PUs) are prevalent in hospitalized patients; they may cause clinical, psychological, and economic problems in these patients. Previous studies are cross-sectional, have used pooled data, or cox-regression models to assess the risk for developing PU. However, PU risk scores change over time and models that account for time varying variables are useful for cohort analysis of data. Aims and Objectives: The present longitudinal study was conducted to compare the risk of PU between surgical and nonsurgical patients, and to evaluate the factors associated with the development of these ulcers over a period of time. Materials and Methods: We evaluated 290 hospitalized patients over a 4 months period. The main outcomes for our analysis were: (1) Score on the pressure risk assessment scale; and (2) the proportion of individuals who were at severe risk for developing PUs. We used random effects models for longitudinal analysis of the data. Results: The mean PU score was significantly higher in the nonsurgical patients compared with surgical patients at baseline (15.23 [3.86] vs. 9.33 [4.57]; P < 0.01). About 7% of the total patients had a score of >20 at baseline and were considered as being at high-risk for PU; the proportion was significantly higher among the nonsurgical patients compared with the surgical patients (14% vs. 4%, P = 0.003). In the adjusted models, there was no difference for severe risk for PU between surgical and nonsurgical patients (odds ratios [ORs]: 0.37, 95% confidence interval [CI]: 0.01–12.80). An additional day in the ward was associated with a significantly higher likelihood of being at high-risk for PU (OR: 1.47, 95% CI: 1.16–1.86). Conclusion: There were no significant differences between patients who were admitted for surgery compared with those who were not. An additional day in the ward, however, is important for developing a high-risk score for PU on the monitoring scale, and these patients require active interventions. PMID:26677269

Toxicokinetics/toxicodynamics of arsenic for farmed juvenile milkfish Chanos chanos and human consumption risk in BFD-endemic area of Taiwan.

PubMed

Chou, Berry Yun-Hua; Liao, Chung-Min; Lin, Ming-Chao; Cheng, Hsu-Hui

2006-05-01

This paper presents a toxicokinetic/toxicodynamic analysis to appraise arsenic (As) bioaccumulation in farmed juvenile milkfish Chanos chanos at blackfoot disease (BFD)-endemic area in Taiwan, whereas probabilistic incremental lifetime cancer risk (ILCR) and hazard quotient (HQ) models are also employed to assess the range of exposures for the fishers and non-fishers who eat the contaminated fish. We conducted a 7-day exposure experiment to obtain toxicokinetic parameters, whereas a simple critical body burden toxicity model was verified with LC50(t) data obtained from a 7-day acute toxicity bioassay. Acute toxicity bioassay indicates that 96-h LC50 for juvenile milkfish exposed to As is 7.29 (95% CI: 3.10-10.47) mg l(-1). Our risk analysis for milkfish reared in BFD-endemic area indicates a low likelihood that survival is being affected by waterborne As. Human risk analysis demonstrates that 90%-tile probability exposure ILCRs for fishers in BFD-endemic area have orders of magnitude of 10(-3), indicating a high potential carcinogenic risk, whereas there is no significant cancer risk for non-fishers (ILCRs around 10(-5)). All predicted 90%-tiles of HQ are less than 1 for non-fishers, yet larger than 10 for fishers which indicate larger contributions from farmed milkfish consumptions. Sensitivity analysis indicates that to increase the accuracy of the results, efforts should focus on a better definition of probability distributions for milkfish daily consumption rate and As level in milkfish. Here we show that theoretical human health risks for consuming As-contaminated milkfish in the BFD-endemic area are alarming under a conservative condition based on a probabilistic risk assessment model.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

Association of glutathione S-transferase pi (GSTP1) Ile105Val polymorphism with the risk of skin cancer: a meta-analysis.

PubMed

Zhou, Cheng-Fan; Ma, Tai; Zhou, Deng-Chuan; Shen, Tong; Zhu, Qi-Xing

2015-08-01

Numerous epidemiological studies have evaluated the association of Glutathione S-transferase P1 (GSTP1) Ile105Val polymorphism with the risk of skin cancer. However, the results remain inconclusive. To derive a more precise estimation of the association between the GSTP1 Ile105Val polymorphism and skin cancer risk, a meta-analysis was performed. A comprehensive search was conducted to identify the eligible studies. We used odds ratios (ORs) with 95 % confidence intervals (CIs) to assess the association of GSTP1 Ile105Val polymorphism with skin cancer risk. Thirteen case-control studies in nine articles, which included a total of 1504 cases and 2243 controls. Overall, we found that GSTP1 Ile105Val polymorphism was not associated with skin cancer risk. Furthermore, subgroup analysis by histological types showed that GSTP1 Ile105Val polymorphism was associated with risks of malignant melanoma under the dominant model (Val/Val + Val/Ile vs. Ile/Ile: OR 1.230, 95 % CI 1.017-1.488, P = 0.033). However, lack of association between GSTP1 Ile105Val polymorphism and BCC and SCC risk in all genetic models. Our meta-analysis suggested that the GSTP1 Ile105Val polymorphism might be associated with increased risk of malignant melanoma in Caucasian population.

Accidental Water Pollution Risk Analysis of Mine Tailings Ponds in Guanting Reservoir Watershed, Zhangjiakou City, China.

PubMed

Liu, Renzhi; Liu, Jing; Zhang, Zhijiao; Borthwick, Alistair; Zhang, Ke

2015-12-02

Over the past half century, a surprising number of major pollution incidents occurred due to tailings dam failures. Most previous studies of such incidents comprised forensic analyses of environmental impacts after a tailings dam failure, with few considering the combined pollution risk before incidents occur at a watershed-scale. We therefore propose Watershed-scale Tailings-pond Pollution Risk Analysis (WTPRA), designed for multiple mine tailings ponds, stemming from previous watershed-scale accidental pollution risk assessments. Transferred and combined risk is embedded using risk rankings of multiple routes of the "source-pathway-target" in the WTPRA. The previous approach is modified using multi-criteria analysis, dam failure models, and instantaneous water quality models, which are modified for application to multiple tailings ponds. The study area covers the basin of Gutanting Reservoir (the largest backup drinking water source for Beijing) in Zhangjiakou City, where many mine tailings ponds are located. The resultant map shows that risk is higher downstream of Gutanting Reservoir and in its two tributary basins (i.e., Qingshui River and Longyang River). Conversely, risk is lower in the midstream and upstream reaches. The analysis also indicates that the most hazardous mine tailings ponds are located in Chongli and Xuanhua, and that Guanting Reservoir is the most vulnerable receptor. Sensitivity and uncertainty analyses are performed to validate the robustness of the WTPRA method.

Milk, yogurt, and lactose intake and ovarian cancer risk: a meta-analysis.

PubMed

Liu, Jing; Tang, Wenru; Sang, Lei; Dai, Xiaoli; Wei, Danping; Luo, Ying; Zhang, Jihong

2015-01-01

Inconclusive information for the role of dairy food intake in relation to ovarian cancer risk may associate with adverse effects of lactose, which has been hypothesized to increase gonadotropin levels in animal models and ecological studies. Up to now, several studies have indicated the association between dairy food intake and risk of ovarian cancer, but no identified founding was reported. We performed this meta-analysis to derive a more precise estimation of the association between dairy food intake and ovarian cancer risk. Using the data from 19 available publications, we examined dairy food including low-fat/skim milk, whole milk, yogurt and lactose in relation to risk of ovarian cancer by meta-analysis. Pooled odds ratio (OR) with 95% confidence interval (CI) were used to assess the association. We observed a slightly increased risk of ovarian cancer with high intake of whole milk, but has no statistical significance (OR = 1.228, 95% CI = 1.031-1.464, P = 0.022). The results of other milk models did not provide evidence of positive association with ovarian cancer risk. This meta-analysis suggests that low-fat/skim milk, whole milk, yogurt and lactose intake has no associated with increased risk of ovarian cancer. Further studies with larger participants worldwide are needed to validate the association between dairy food intake and ovarian cancer.

Maternal dietary nitrate intake and risk of neural tube defects: A systematic review and dose-response meta-analysis.

PubMed

Kakavandi, Nader Rahimi; Hasanvand, Amin; Ghazi-Khansari, Mahmoud; Sezavar, Ahmad Habibian; Nabizadeh, Hassan; Parohan, Mohammad

2018-05-12

Despite growing evidence for the potential teratogenicity of nitrate, knowledge about the dose-response relationship of dietary nitrate intake and risk of specific birth defects such as neural tube defects (NTDs) is limited. Therefore, the aim of this meta-analysis was to synthesize the knowledge about the dose-response relation between maternal dietary nitrate intake and the risk of NTDs. We conducted a systematic search in PubMed, ISI Web of Science and Scopus up to February 2018 for observational studies. Risk ratios (RRs) and 95% confidence intervals (95% CI) were calculated using a random-effects model for highest versus lowest intake categories. The linear and non-linear relationships between nitrate intake and risk of NTDs were also investigated. Overall, 5 studies were included in the meta-analyses. No association was observed between nitrate intake and NTDs risk in high versus low intake (RR: 1.33; 95% CI: 0.89-1.99, p = 0.158) and linear dose-response (RR: 1.03; 95% CI: 0.99-1.07, p = 0.141) meta-analysis. However, there were positive relationships between nitrate intake and risk of NTDs in non-linear (p non-linearity <0.05) model. Findings from this dose-response meta-analysis indicate that maternal nitrate intake higher than ∼3 mg/day is positively associated with NTDs risk. Copyright © 2018 Elsevier Ltd. All rights reserved.

Accidental Water Pollution Risk Analysis of Mine Tailings Ponds in Guanting Reservoir Watershed, Zhangjiakou City, China

PubMed Central

Liu, Renzhi; Liu, Jing; Zhang, Zhijiao; Borthwick, Alistair; Zhang, Ke

2015-01-01

Over the past half century, a surprising number of major pollution incidents occurred due to tailings dam failures. Most previous studies of such incidents comprised forensic analyses of environmental impacts after a tailings dam failure, with few considering the combined pollution risk before incidents occur at a watershed-scale. We therefore propose Watershed-scale Tailings-pond Pollution Risk Analysis (WTPRA), designed for multiple mine tailings ponds, stemming from previous watershed-scale accidental pollution risk assessments. Transferred and combined risk is embedded using risk rankings of multiple routes of the “source-pathway-target” in the WTPRA. The previous approach is modified using multi-criteria analysis, dam failure models, and instantaneous water quality models, which are modified for application to multiple tailings ponds. The study area covers the basin of Gutanting Reservoir (the largest backup drinking water source for Beijing) in Zhangjiakou City, where many mine tailings ponds are located. The resultant map shows that risk is higher downstream of Gutanting Reservoir and in its two tributary basins (i.e., Qingshui River and Longyang River). Conversely, risk is lower in the midstream and upstream reaches. The analysis also indicates that the most hazardous mine tailings ponds are located in Chongli and Xuanhua, and that Guanting Reservoir is the most vulnerable receptor. Sensitivity and uncertainty analyses are performed to validate the robustness of the WTPRA method. PMID:26633450

Risk analysis of emergent water pollution accidents based on a Bayesian Network.

PubMed

Tang, Caihong; Yi, Yujun; Yang, Zhifeng; Sun, Jie

2016-01-01

To guarantee the security of water quality in water transfer channels, especially in open channels, analysis of potential emergent pollution sources in the water transfer process is critical. It is also indispensable for forewarnings and protection from emergent pollution accidents. Bridges above open channels with large amounts of truck traffic are the main locations where emergent accidents could occur. A Bayesian Network model, which consists of six root nodes and three middle layer nodes, was developed in this paper, and was employed to identify the possibility of potential pollution risk. Dianbei Bridge is reviewed as a typical bridge on an open channel of the Middle Route of the South to North Water Transfer Project where emergent traffic accidents could occur. Risk of water pollutions caused by leakage of pollutants into water is focused in this study. The risk for potential traffic accidents at the Dianbei Bridge implies a risk for water pollution in the canal. Based on survey data, statistical analysis, and domain specialist knowledge, a Bayesian Network model was established. The human factor of emergent accidents has been considered in this model. Additionally, this model has been employed to describe the probability of accidents and the risk level. The sensitive reasons for pollution accidents have been deduced. The case has also been simulated that sensitive factors are in a state of most likely to lead to accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-01-01

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

Associations between polymorphisms of the ADIPOQ gene and hypertension risk: a systematic and meta-analysis

PubMed Central

Fan, Weina; Qu, Xiaowei; Li, Jing; Wang, Xingning; Bai, Yanping; Cao, Qingmei; Ma, Liqun; Zhou, Xiaoyao; Zhu, Wei; Liu, Wei; Ma, Qiang

2017-01-01

ADIPOQ gene polymorphisms have been indicated to be associated with hypertension; however, published studies have reported inconsistent results. Eligible studies were retrieved by searching the PubMed, Embase and China National Knowledge Infrastructure databases. The case group consisted of patients with hypertension, and the control group consisted of subjects with normal blood pressure. Based on eleven published articles, involving 4837 cases and 5618 controls, the pooled results from rs2241766 polymorphism showed increased risk in the allelic model (G VS T: OR = 1.16, 95%CI = 1.06–1.27), recessive model (GG VS GT + TT: OR = 1.34, 95%CI = 1.10–1.63), dominant model (GG + GT VS TT: OR = 1.15, 95%CI = 1.02–1.30) and homozygote model (GG VS TT: OR = 1.38, 95%CI = 1.21–1.69). In addition, rs266729 polymorphism showed increased risk for hypertension in the recessive model (GG VS GC + CC: OR = 1.43, 95%CI = 1.02–2.01). In the Caucasian subgroup, rs1501299 polymorphism showed decreased risk of hypertension in the allelic model (T VS G: OR = 0.75, 95%CI = 0.58–0.97), dominant model (TT + TG VS GG: OR = 0.83, 95%CI = 0.71–0.98) and heterozygote model (TG VS GG: OR = 0.82, 95%CI = 0.68–0.99). The rs2241766 polymorphism was associated with a significant increase in hypertension risk based on our analysis. Moreover, an increased risk of rs266729 in hypertension patients was also detected. Our meta-analysis suggests that the rs1501299 polymorphism may play a protective role in hypertension in Caucasian subgroup; however, this finding requires further study. PMID:28181566

The use of aquatic bioconcentration factors in ecological risk assessments: Confounding issues, laboratory v/s modeled results

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

Brandt, C.; Blanton, M.L.; Dirkes, R.

1995-12-31

Bioconcentration in aquatic systems is generally taken to refer to contaminant uptake through non-ingestion pathways (i.e., dermal and respiration uptake). Ecological risk assessments performed on aquatic systems often rely on published data on bioconcentration factors to calibrate models of exposure. However, many published BCFs, especially those from in situ studies, are confounded by uptake from ingestion of prey. As part of exposure assessment and risk analysis of the Columbia River`s Hanford Reach, the authors tested a methodology to estimate radionuclide BCFs for several aquatic species in the Hanford Reach of the Columbia River. The iterative methodology solves for BCFs frommore » known body burdens and environmental media concentrations. This paper provides BCF methodology description comparisons of BCF from literature and modeled values and how they were used in the exposure assessment and risk analysis of the Columbia River`s Hanford Reach.« less

Using incident response trees as a tool for risk management of online financial services.

PubMed

Gorton, Dan

2014-09-01

The article introduces the use of probabilistic risk assessment for modeling the incident response process of online financial services. The main contribution is the creation of incident response trees, using event tree analysis, which provides us with a visual tool and a systematic way to estimate the probability of a successful incident response process against the currently known risk landscape, making it possible to measure the balance between front-end and back-end security measures. The model is presented using an illustrative example, and is then applied to the incident response process of a Swedish bank. Access to relevant data is verified and the applicability and usability of the proposed model is verified using one year of historical data. Potential advantages and possible shortcomings are discussed, referring to both the design phase and the operational phase, and future work is presented. © 2014 Society for Risk Analysis.

A formal framework of scenario creation and analysis of extreme hydrological events

NASA Astrophysics Data System (ADS)

Lohmann, D.

2007-12-01

We are presenting a formal framework for a hydrological risk analysis. Different measures of risk will be introduced, such as average annual loss or occurrence exceedance probability. These are important measures for e.g. insurance companies to determine the cost of insurance. One key aspect of investigating the potential consequences of extreme hydrological events (floods and draughts) is the creation of meteorological scenarios that reflect realistic spatial and temporal patterns of precipitation that also have correct local statistics. 100,000 years of these meteorological scenarios are used in a calibrated rainfall-runoff-flood-loss-risk model to produce flood and draught events that have never been observed. The results of this hazard model are statistically analyzed and linked to socio-economic data and vulnerability functions to show the impact of severe flood events. We are showing results from the Risk Management Solutions (RMS) Europe Flood Model to introduce this formal framework.

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system components, part 2

NASA Technical Reports Server (NTRS)

1991-01-01

The technical effort and computer code enhancements performed during the sixth year of the Probabilistic Structural Analysis Methods program are summarized. Various capabilities are described to probabilistically combine structural response and structural resistance to compute component reliability. A library of structural resistance models is implemented in the Numerical Evaluations of Stochastic Structures Under Stress (NESSUS) code that included fatigue, fracture, creep, multi-factor interaction, and other important effects. In addition, a user interface was developed for user-defined resistance models. An accurate and efficient reliability method was developed and was successfully implemented in the NESSUS code to compute component reliability based on user-selected response and resistance models. A risk module was developed to compute component risk with respect to cost, performance, or user-defined criteria. The new component risk assessment capabilities were validated and demonstrated using several examples. Various supporting methodologies were also developed in support of component risk assessment.

Managing security risks for inter-organisational information systems: a multiagent collaborative model

NASA Astrophysics Data System (ADS)

Feng, Nan; Wu, Harris; Li, Minqiang; Wu, Desheng; Chen, Fuzan; Tian, Jin

2016-09-01

Information sharing across organisations is critical to effectively managing the security risks of inter-organisational information systems. Nevertheless, few previous studies on information systems security have focused on inter-organisational information sharing, and none have studied the sharing of inferred beliefs versus factual observations. In this article, a multiagent collaborative model (MACM) is proposed as a practical solution to assess the risk level of each allied organisation's information system and support proactive security treatment by sharing beliefs on event probabilities as well as factual observations. In MACM, for each allied organisation's information system, we design four types of agents: inspection agent, analysis agent, control agent, and communication agent. By sharing soft findings (beliefs) in addition to hard findings (factual observations) among the organisations, each organisation's analysis agent is capable of dynamically predicting its security risk level using a Bayesian network. A real-world implementation illustrates how our model can be used to manage security risks in distributed information systems and that sharing soft findings leads to lower expected loss from security risks.

Framework for Risk Analysis in Multimedia Environmental Systems: Modeling Individual Steps of a Risk Assessment Process

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

Shah, Anuj; Castleton, Karl J.; Hoopes, Bonnie L.

2004-06-01

The study of the release and effects of chemicals in the environment and their associated risks to humans is central to public and private decision making. FRAMES 1.X, Framework for Risk Analysis in Multimedia Environmental Systems, is a systems modeling software platform, developed by PNNL, Pacific Northwest National Laboratory, that helps scientists study the release and effects of chemicals on a source to outcome basis, create environmental models for similar risk assessment and management problems. The unique aspect of FRAMES is to dynamically introduce software modules representing individual components of a risk assessment (e.g., source release of contaminants, fate andmore » transport in various environmental media, exposure, etc.) within a software framework, manipulate their attributes and run simulations to obtain results. This paper outlines the fundamental constituents of FRAMES 2.X, an enhanced version of FRAMES 1.X, that greatly improve the ability of the module developers to “plug” their self-developed software modules into the system. The basic design, the underlying principles and a discussion of the guidelines for module developers are presented.« less

Communicating Pacific Rim Risk: A GIS Analysis of Hazard, Vulnerability, Population, and Infrastructure

NASA Astrophysics Data System (ADS)

Yurkovich, E. S.; Howell, D. G.

2002-12-01

Exploding population and unprecedented urban development within the last century helped fuel an increase in the severity of natural disasters. Not only has the world become more populated, but people, information and commodities now travel greater distances to service larger concentrations of people. While many of the earth's natural hazards remain relatively constant, understanding the risk to increasingly interconnected and large populations requires an expanded analysis. To improve mitigation planning we propose a model that is accessible to planners and implemented with public domain data and industry standard GIS software. The model comprises 1) the potential impact of five significant natural hazards: earthquake, flood, tropical storm, tsunami and volcanic eruption assessed by a comparative index of risk, 2) population density, 3) infrastructure distribution represented by a proxy, 4) the vulnerability of the elements at risk (population density and infrastructure distribution) and 5) the connections and dependencies of our increasingly 'globalized' world, portrayed by a relative linkage index. We depict this model with the equation, Risk = f(H, E, V, I) Where H is an index normalizing the impact of five major categories of natural hazards; E is one element at risk, population or infrastructure; V is a measure of the vulnerability for of the elements at risk; and I pertains to a measure of interconnectivity of the elements at risk as a result of economic and social globalization. We propose that future risk analysis include the variable I to better define and quantify risk. Each assessment reflects different repercussions from natural disasters: losses of life or economic activity. Because population and infrastructure are distributed heterogeneously across the Pacific region, two contrasting representations of risk emerge from this study.

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management.

PubMed

Oddo, Perry C; Lee, Ben S; Garner, Gregory G; Srikrishnan, Vivek; Reed, Patrick M; Forest, Chris E; Keller, Klaus

2017-09-05

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies. © 2017 Society for Risk Analysis.

The quandaries and promise of risk management: a scientist's perspective on integration of science and management.

Treesearch

B.G. Marcot

2007-01-01

This paper briefly lists constraints and problems of traditional approaches to natural resource risk analysis and risk management. Such problems include disparate definitions of risk, multiple and conflicting objectives and decisions, conflicting interpretations of uncertainty, and failure of articulating decision criteria, risk attitudes, modeling assumptions, and...

Costing the satellite power system

NASA Technical Reports Server (NTRS)

Hazelrigg, G. A., Jr.

1978-01-01

The paper presents a methodology for satellite power system costing, places approximate limits on the accuracy possible in cost estimates made at this time, and outlines the use of probabilistic cost information in support of the decision-making process. Reasons for using probabilistic costing or risk analysis procedures instead of standard deterministic costing procedures are considered. Components of cost, costing estimating relationships, grass roots costing, and risk analysis are discussed. Risk analysis using a Monte Carlo simulation model is used to estimate future costs.

A methodology for modeling regional terrorism risk.

PubMed

Chatterjee, Samrat; Abkowitz, Mark D

2011-07-01

Over the past decade, terrorism risk has become a prominent consideration in protecting the well-being of individuals and organizations. More recently, there has been interest in not only quantifying terrorism risk, but also placing it in the context of an all-hazards environment in which consideration is given to accidents and natural hazards, as well as intentional acts. This article discusses the development of a regional terrorism risk assessment model designed for this purpose. The approach taken is to model terrorism risk as a dependent variable, expressed in expected annual monetary terms, as a function of attributes of population concentration and critical infrastructure. This allows for an assessment of regional terrorism risk in and of itself, as well as in relation to man-made accident and natural hazard risks, so that mitigation resources can be allocated in an effective manner. The adopted methodology incorporates elements of two terrorism risk modeling approaches (event-based models and risk indicators), producing results that can be utilized at various jurisdictional levels. The validity, strengths, and limitations of the model are discussed in the context of a case study application within the United States. © 2011 Society for Risk Analysis.

Association between estrogen receptora gene (ESR1) PvuII (T/C) and XbaI (A/G) polymorphisms and premature ovarian failure risk: evidence from a meta-analysis.

PubMed

He, Meirong; Shu, Jingcheng; Huang, Xing; Tang, Hui

2015-02-01

Genetic factors are important in the pathogenesis of Premature ovarian failure (POF). Notably, estrogen receptor-a (ESR1) has been suggested as a possible candidate gene for POF; however, published studies of ESR1 gene polymorphisms have been hampered by small sample sizes and inconclusive or ambiguous results. The aim of this meta analysis is to investigate the associations between two novel common ESR1 polymorphisms (intron 1 polymorphisms PvuII-rs2234693: T.C and XbaI-rs9340799: A.G) and POF. A comprehensive search was conducted to identify all studies on the association of ESR1 gene polymorphisms with POF up to August 2014. Pooled odds ratio (OR) and corresponding 95 % confidence interval (CI) were calculated using fixed-or random-effects model in the meta-analysis. Three studies covering 1396 subjects were identified. Pooled data showed significant association between ESR1 gene PvuII polymorphism and risk of POF: [allele model: Cvs. T, OR = 0.735, 95%CI: 0.624 ~ 0.865, p = 0.001; co-dominant models: CCvs.TT, OR = 0.540, 95%CI: 0.382 ~ 0.764, p = 0.001, CTvs.TT, OR = 0.735, 95%CI: 0.555 ~ 0.972, p = 0.031; dominant model: CT + CCvs.TT, OR = 0.618, 95%CI: 0.396 ~ 0.966, p = 0.035; recessive model: CCvs.TT + CT, OR = 0.659, 95%CI: 0.502 ~ 0.864, p = 0.003]. Subgroup analyses showed a significant association in all models in Asian population, but no significant association in any model in European population. For the XbaI polymorphism, overall, no significant association was observed under any genetic models. However, under dominant model, ESR1 gene XbaI polymorphism is significantly association with risk of POF in Asian population. The present meta-analysis suggests that ESR1gene PvuII polymorphism is significantly associated with an increased risk of POF. And ESR1gene XbaI polymorphism is not association with risk of POF overall. However, under dominant model, ESR1gene XbaI polymorphism is significantly association with risk of POF in Asian population. Further large and well-designed studies are needed to confirm the association.

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).

Application and Validation of a GIS Model for Local Tsunami Vulnerability and Mortality Risk Analysis

NASA Astrophysics Data System (ADS)

Harbitz, C. B.; Frauenfelder, R.; Kaiser, G.; Glimsdal, S.; Sverdrup-thygeson, K.; Løvholt, F.; Gruenburg, L.; Mc Adoo, B. G.

2015-12-01

The 2011 Tōhoku tsunami caused a high number of fatalities and massive destruction. Data collected after the event allow for retrospective analyses. Since 2009, NGI has developed a generic GIS model for local analyses of tsunami vulnerability and mortality risk. The mortality risk convolves the hazard, exposure, and vulnerability. The hazard is represented by the maximum tsunami flow depth (with a corresponding likelihood), the exposure is described by the population density in time and space, while the vulnerability is expressed by the probability of being killed as a function of flow depth and building class. The analysis is further based on high-resolution DEMs. Normally a certain tsunami scenario with a corresponding return period is applied for vulnerability and mortality risk analysis. Hence, the model was first employed for a tsunami forecast scenario affecting Bridgetown, Barbados, and further developed in a forecast study for the city of Batangas in the Philippines. Subsequently, the model was tested by hindcasting the 2009 South Pacific tsunami in American Samoa. This hindcast was based on post-tsunami information. The GIS model was adapted for optimal use of the available data and successfully estimated the degree of mortality.For further validation and development, the model was recently applied in the RAPSODI project for hindcasting the 2011 Tōhoku tsunami in Sendai and Ishinomaki. With reasonable choices of building vulnerability, the estimated expected number of fatalities agree well with the reported death toll. The results of the mortality hindcast for the 2011 Tōhoku tsunami substantiate that the GIS model can help to identify high tsunami mortality risk areas, as well as identify the main risk drivers.The research leading to these results has received funding from CONCERT-Japan Joint Call on Efficient Energy Storage and Distribution/Resilience against Disasters (http://www.concertjapan.eu; project RAPSODI - Risk Assessment and design of Prevention Structures fOr enhanced tsunami DIsaster resilience http://www.ngi.no/en/Project-pages/RAPSODI/), and from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 603839 (Project ASTARTE - Assessment, STrategy And Risk reduction for Tsunamis in Europe http://www.astarte-project.eu/).

Methylenetetrahydrofolate reductase polymorphism C677T is a protective factor for pediatric acute lymphoblastic leukemia in the Chinese population: a meta-analysis.

PubMed

Wang, Haigang; Meng, Lujing; Zhao, Lixia; Wang, Jiali; Liu, Xinchun; Mi, Wenjie

2012-12-01

Two polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene, C677T and A1298C, were hypothesized to decrease the risk of acute lymphoblastic leukemia (ALL). Studies examining the associations between these two polymorphisms and ALL susceptibility drew inconsistent results. To obtain a reliable conclusion in a Chinese population, we carried out a meta-analysis. In total, 11 studies on C677T polymorphism (1597 cases and 2295 controls) and 10 studies on A1298C polymorphism (1553 cases and 2224 controls) were included in the meta-analysis. We found a significant association between the 677T variant and reduced ALL risk in Chinese children (Dominant model: odds ratio [OR(FE)]=0.73, 95% confidence interval [CI]: 0.63-0.86, p<0.01). Heterogeneity between the studies in the children subgroup was weak and vanished after excluding one study deviating from HWE in the control group (p>0.1). In the adult subgroup, there was no significant association between the C677T variant and ALL risk (Dominant model: OR(RE)=0.88, 95% CI: 0.45-1.72, p=0.72). Significant heterogeneity was found in the adult subgroup in all the genetic model tests (p<0.1). The A1298C polymorphism had an effect on ALL risk neither in adults (Dominant model: OR(FE)=0.95, 95% CI: 0.71-1.27, p=0.72) nor in children (Dominant model: OR(FE)=1.02, 95% CI: 0.87-1.21, p=0.77). No significant heterogeneity between studies on A1298C polymorphism was found in the meta-analysis (p>0.1). The results showed that there was a protective effect of the MTHFR C677T variant on ALL risk in Chinese children.

Segregation analysis of cryptogenic epilepsy and an empirical test of the validity of the results

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

Ottman, R.; Hauser, W.A.; Barker-Cummings, C.

1997-03-01

We used POINTER to perform segregation analysis of crytogenic epilepsy in 1,557 three-generation families (probands and their parents, siblings, and offspring) ascertained from voluntary organizations. Analysis of the full data set indicated that the data were most consistent with an autosomal dominant (AD) model with 61% penetrance of the susceptibility gene. However, subsequent analyses revealed that the patterns of familial aggregation differed markedly between siblings and offspring of the probands. Risks in siblings were consistent with an autosomal recessive (AR) model and inconsistent with an AD model, whereas risks in offspring were inconsistent with an AR model and more consistentmore » with an AD model. As a further test of the validity of the AD model, we used sequential ascertainment to extend the family history information in the subset of families judged likely to carry the putative susceptibility gene because they contained at least three affected individuals. Prevalence of idiopathic/cryptogenic epilepsy was only 3.7% in newly identified relatives expected to have a 50% probability of carrying the susceptibility gene under an AD model. Approximately 30% (i.e., 50% X 61%) were expected to be affected under the AD model resulting from the segregation analysis. These results suggest that the familial distribution of cryptogenic epilepsy is inconsistent with any conventional genetic model. The differences between siblings and offspring in the patterns of familial risk are intriguing and should be investigated further. 28 refs., 6 tabs.« less

Train integrity detection risk analysis based on PRISM

NASA Astrophysics Data System (ADS)

Wen, Yuan

2018-04-01

GNSS based Train Integrity Monitoring System (TIMS) is an effective and low-cost detection scheme for train integrity detection. However, as an external auxiliary system of CTCS, GNSS may be influenced by external environments, such as uncertainty of wireless communication channels, which may lead to the failure of communication and positioning. In order to guarantee the reliability and safety of train operation, a risk analysis method of train integrity detection based on PRISM is proposed in this article. First, we analyze the risk factors (in GNSS communication process and the on-board communication process) and model them. Then, we evaluate the performance of the model in PRISM based on the field data. Finally, we discuss how these risk factors influence the train integrity detection process.

A new model for care population management.

PubMed

Williams, Jeni

2013-03-01

Steps toward building a population management model of care should include: Identifying the population that would be cared for through a population management initiative. Conducting an actuarial analysis for this population, reviewing historical utilization and cost data and projecting changes in utilization. Investing in data infrastructure that supports the exchange of data among providers and with payers. Determining potential exposure to downside risk and organizational capacity to assume this risk. Experimenting with payment models and care delivery approaches Hiring care coordinators to manage care for high-risk patients.

Software for occupational health and safety risk analysis based on a fuzzy model.

PubMed

Stefanovic, Miladin; Tadic, Danijela; Djapan, Marko; Macuzic, Ivan

2012-01-01

Risk and safety management are very important issues in healthcare systems. Those are complex systems with many entities, hazards and uncertainties. In such an environment, it is very hard to introduce a system for evaluating and simulating significant hazards. In this paper, we analyzed different types of hazards in healthcare systems and we introduced a new fuzzy model for evaluating and ranking hazards. Finally, we presented a developed software solution, based on the suggested fuzzy model for evaluating and monitoring risk.

Replica Approach for Minimal Investment Risk with Cost

NASA Astrophysics Data System (ADS)

Shinzato, Takashi

2018-06-01

In the present work, the optimal portfolio minimizing the investment risk with cost is discussed analytically, where an objective function is constructed in terms of two negative aspects of investment, the risk and cost. We note the mathematical similarity between the Hamiltonian in the mean-variance model and the Hamiltonians in the Hopfield model and the Sherrington-Kirkpatrick model, show that we can analyze this portfolio optimization problem by using replica analysis, and derive the minimal investment risk with cost and the investment concentration of the optimal portfolio. Furthermore, we validate our proposed method through numerical simulations.

Forecasting risk along a river basin using a probabilistic and deterministic model for environmental risk assessment of effluents through ecotoxicological evaluation and GIS.

PubMed

Gutiérrez, Simón; Fernandez, Carlos; Barata, Carlos; Tarazona, José Vicente

2009-12-20

This work presents a computer model for Risk Assessment of Basins by Ecotoxicological Evaluation (RABETOX). The model is based on whole effluent toxicity testing and water flows along a specific river basin. It is capable of estimating the risk along a river segment using deterministic and probabilistic approaches. The Henares River Basin was selected as a case study to demonstrate the importance of seasonal hydrological variations in Mediterranean regions. As model inputs, two different ecotoxicity tests (the miniaturized Daphnia magna acute test and the D.magna feeding test) were performed on grab samples from 5 waste water treatment plant effluents. Also used as model inputs were flow data from the past 25 years, water velocity measurements and precise distance measurements using Geographical Information Systems (GIS). The model was implemented into a spreadsheet and the results were interpreted and represented using GIS in order to facilitate risk communication. To better understand the bioassays results, the effluents were screened through SPME-GC/MS analysis. The deterministic model, performed each month during one calendar year, showed a significant seasonal variation of risk while revealing that September represents the worst-case scenario with values up to 950 Risk Units. This classifies the entire area of study for the month of September as "sublethal significant risk for standard species". The probabilistic approach using Monte Carlo analysis was performed on 7 different forecast points distributed along the Henares River. A 0% probability of finding "low risk" was found at all forecast points with a more than 50% probability of finding "potential risk for sensitive species". The values obtained through both the deterministic and probabilistic approximations reveal the presence of certain substances, which might be causing sublethal effects in the aquatic species present in the Henares River.

Breakthrough seizures—Further analysis of the Standard versus New Antiepileptic Drugs (SANAD) study

PubMed Central

Powell, Graham A.; Tudur Smith, Catrin; Marson, Anthony G.

2017-01-01

Objectives To develop prognostic models for risk of a breakthrough seizure, risk of seizure recurrence after a breakthrough seizure, and likelihood of achieving 12-month remission following a breakthrough seizure. A breakthrough seizure is one that occurs following at least 12 months remission whilst on treatment. Methods We analysed data from the SANAD study. This long-term randomised trial compared treatments for participants with newly diagnosed epilepsy. Multivariable Cox models investigated how clinical factors affect the probability of each outcome. Best fitting multivariable models were produced with variable reduction by Akaike’s Information Criterion. Risks associated with combinations of risk factors were calculated from each multivariable model. Results Significant factors in the multivariable model for risk of a breakthrough seizure following 12-month remission were number of tonic-clonic seizures by achievement of 12-month remission, time taken to achieve 12-month remission, and neurological insult. Significant factors in the model for risk of seizure recurrence following a breakthrough seizure were total number of drugs attempted to achieve 12-month remission, time to achieve 12-month remission prior to breakthrough seizure, and breakthrough seizure treatment decision. Significant factors in the model for likelihood of achieving 12-month remission after a breakthrough seizure were gender, age at breakthrough seizure, time to achieve 12-month remission prior to breakthrough, and breakthrough seizure treatment decision. Conclusions This is the first analysis to consider risk of a breakthrough seizure and subsequent outcomes. The described models can be used to identify people most likely to have a breakthrough seizure, a seizure recurrence following a breakthrough seizure, and to achieve 12-month remission following a breakthrough seizure. The results suggest that focussing on achieving 12-month remission swiftly represents the best therapeutic aim to reduce the risk of a breakthrough seizure and subsequent negative outcomes. This will aid individual patient risk stratification and the design of future epilepsy trials. PMID:29267375

Monitoring of human brain functions in risk decision-making task by diffuse optical tomography using voxel-wise general linear model

NASA Astrophysics Data System (ADS)

Lin, Zi-Jing; Li, Lin; Cazzell, Marry; Liu, Hanli

2013-03-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique which measures the hemodynamic changes that reflect the brain activity. Diffuse optical tomography (DOT), a variant of fNIRS with multi-channel NIRS measurements, has demonstrated capability of three dimensional (3D) reconstructions of hemodynamic changes due to the brain activity. Conventional method of DOT image analysis to define the brain activation is based upon the paired t-test between two different states, such as resting-state versus task-state. However, it has limitation because the selection of activation and post-activation period is relatively subjective. General linear model (GLM) based analysis can overcome this limitation. In this study, we combine the 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with the risk-decision making process. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The balloon analogue risk task (BART) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks. We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making. Voxel-wise GLM analysis was performed on 18human participants (10 males and 8females).In this work, we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT. Results have shown significant changes in the dorsal lateral prefrontal cortex (DLPFC) during the active choice mode and a different hemodynamic pattern between genders, which are in good agreements with published literatures in functional magnetic resonance imaging (fMRI) and fNIRS studies.

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.

Finding Groups Using Model-Based Cluster Analysis: Heterogeneous Emotional Self-Regulatory Processes and Heavy Alcohol Use Risk

ERIC Educational Resources Information Center

Mun, Eun Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

2008-01-01

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the…

Clostridium Difficile Infection Due to Pneumonia Treatment: Mortality Risk Models.

PubMed

Chmielewska, M; Zycinska, K; Lenartowicz, B; Hadzik-Błaszczyk, M; Cieplak, M; Kur, Z; Wardyn, K A

2017-01-01

One of the most common gastrointestinal infection after the antibiotic treatment of community or nosocomial pneumonia is caused by the anaerobic spore Clostridium difficile (C. difficile). The aim of this study was to retrospectively assess mortality due to C. difficile infection (CDI) in patients treated for pneumonia. We identified 94 cases of post-pneumonia CDI out of the 217 patients with CDI. The mortality issue was addressed by creating a mortality risk models using logistic regression and multivariate fractional polynomial analysis. The patients' demographics, clinical features, and laboratory results were taken into consideration. To estimate the influence of the preceding respiratory infection, a pneumonia severity scale was included in the analysis. The analysis showed two statistically significant and clinically relevant mortality models. The model with the highest prognostic strength entailed age, leukocyte count, serum creatinine and urea concentration, hematocrit, coexisting neoplasia or chronic obstructive pulmonary disease. In conclusion, we report on two prognostic models, based on clinically relevant factors, which can be of help in predicting mortality risk in C. difficile infection, secondary to the antibiotic treatment of pneumonia. These models could be useful in preventive tailoring of individual therapy.

Analysis of Young Men: Chapter Two. Determinants of Adult Socioeconomic Attainment in Young Men: An Analysis of the Role of Risk and Social Capital Factors, and the Pathways through Which They Have Their Impacts.

ERIC Educational Resources Information Center

Brown, Brett V.

In this chapter, a series of nested regression models are estimated to analyze three measures of adult socioeconomic attainment measured at age 29: (1) educational attainment; (2) occupational attainment; and (3) earnings. The models seek to relate risk, social capital, social-psychological factors, and life course events in early adulthood, both…

Risk of fatal amebic meningoencephalitis from waterborne Naegleria fowleri

NASA Astrophysics Data System (ADS)

Hallenbeck, William H.; Brenniman, Gary R.

1989-03-01

Primary amebic meningoencephalitis (PAM) is a fatal disease of the central nervous system caused primarily by the free-living ameba, Naegleria fowleri. PAM is primarily associated with swimming in various types of fresh water. World literature was reviewed in order to derive a risk analysis model that would be helpful in the management of PAM. The management of PAM risk is difficult, and the prevention of PAM is almost impossible. However, it is reassuring that the cases and risks estimated by the risk model are usually small, with individual annual risk on the order of 10-6.

Multi-hazard risk analysis related to hurricanes

NASA Astrophysics Data System (ADS)

Lin, Ning

Hurricanes present major hazards to the United States. Associated with extreme winds, heavy rainfall, and storm surge, landfalling hurricanes often cause enormous structural damage to coastal regions. Hurricane damage risk assessment provides the basis for loss mitigation and related policy-making. Current hurricane risk models, however, often oversimplify the complex processes of hurricane damage. This dissertation aims to improve existing hurricane risk assessment methodology by coherently modeling the spatial-temporal processes of storm landfall, hazards, and damage. Numerical modeling technologies are used to investigate the multiplicity of hazards associated with landfalling hurricanes. The application and effectiveness of current weather forecasting technologies to predict hurricane hazards is investigated. In particular, the Weather Research and Forecasting model (WRF), with Geophysical Fluid Dynamics Laboratory (GFDL)'s hurricane initialization scheme, is applied to the simulation of the wind and rainfall environment during hurricane landfall. The WRF model is further coupled with the Advanced Circulation (AD-CIRC) model to simulate storm surge in coastal regions. A case study examines the multiple hazards associated with Hurricane Isabel (2003). Also, a risk assessment methodology is developed to estimate the probability distribution of hurricane storm surge heights along the coast, particularly for data-scarce regions, such as New York City. This methodology makes use of relatively simple models, specifically a statistical/deterministic hurricane model and the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, to simulate large numbers of synthetic surge events, and conducts statistical analysis. The estimation of hurricane landfall probability and hazards are combined with structural vulnerability models to estimate hurricane damage risk. Wind-induced damage mechanisms are extensively studied. An innovative windborne debris risk model is developed based on the theory of Poisson random measure, substantiated by a large amount of empirical data. An advanced vulnerability assessment methodology is then developed, by integrating this debris risk model and a component-based pressure damage model, to predict storm-specific or annual damage to coastal residential neighborhoods. The uniqueness of this vulnerability model lies in its detailed description of the interaction between wind pressure and windborne debris effects over periods of strong winds, which is a major mechanism leading to structural failures during hurricanes.

Effects of changes along the risk chain on flood risk

NASA Astrophysics Data System (ADS)

Duha Metin, Ayse; Apel, Heiko; Viet Dung, Nguyen; Guse, Björn; Kreibich, Heidi; Schröter, Kai; Vorogushyn, Sergiy; Merz, Bruno

2017-04-01

Interactions of hydrological and socio-economic factors shape flood disaster risk. For this reason, assessment of flood risk ideally takes into account the whole flood risk chain from atmospheric processes, through the catchment and river system processes to the damage mechanisms in the affected areas. Since very different processes at various scales are interacting along the flood risk, the impact of the single components is rather unclear. However for flood risk management, it is required to know the controlling factor of flood damages. The present study, using the flood-prone Mulde catchment in Germany, discusses the sensitivity of flood risk to disturbances along the risk chain: How do disturbances propagate through the risk chain? How do different disturbances combine or conflict and affect flood risk? In this sensitivity analysis, the five components of the flood risk change are included. These are climate, catchment, river system, exposure and vulnerability. A model framework representing the complete risk chain is combined with observational data to understand how the sensitivities evolve along the risk chain by considering three plausible change scenarios for each of five components. The flood risk is calculated by using the Regional Flood Model (RFM) which is based on a continuous simulation approach, including rainfall-runoff, 1D river network, 2D hinterland inundation and damage estimation models. The sensitivity analysis covers more than 240 scenarios with different combinations of the five components. It is investigated how changes in different components affect risk indicators, such as the risk curve and expected annual damage (EAD). In conclusion, it seems that changes in exposure and vulnerability seem to outweigh changes in hazard.

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.

Uncertainty Analysis of A Flood Risk Mapping Procedure Applied In Urban Areas

NASA Astrophysics Data System (ADS)

Krause, J.; Uhrich, S.; Bormann, H.; Diekkrüger, B.

In the framework of IRMA-Sponge program the presented study was part of the joint research project FRHYMAP (flood risk and hydrological mapping). A simple con- ceptual flooding model (FLOODMAP) has been developed to simulate flooded areas besides rivers within cities. FLOODMAP requires a minimum of input data (digital el- evation model (DEM), river line, water level plain) and parameters and calculates the flood extent as well as the spatial distribution of flood depths. of course the simulated model results are affected by errors and uncertainties. Possible sources of uncertain- ties are the model structure, model parameters and input data. Thus after the model validation (comparison of simulated water to observed extent, taken from airborne pictures) the uncertainty of the essential input data set (digital elevation model) was analysed. Monte Carlo simulations were performed to assess the effect of uncertain- ties concerning the statistics of DEM quality and to derive flooding probabilities from the set of simulations. The questions concerning a minimum resolution of a DEM re- quired for flood simulation and concerning the best aggregation procedure of a given DEM was answered by comparing the results obtained using all available standard GIS aggregation procedures. Seven different aggregation procedures were applied to high resolution DEMs (1-2m) in three cities (Bonn, Cologne, Luxembourg). Basing on this analysis the effect of 'uncertain' DEM data was estimated and compared with other sources of uncertainties. Especially socio-economic information and monetary transfer functions required for a damage risk analysis show a high uncertainty. There- fore this study helps to analyse the weak points of the flood risk and damage risk assessment procedure.

A concept analysis of forensic risk.

PubMed

Kettles, A M

2004-08-01

Forensic risk is a term used in relation to many forms of clinical practice, such as assessment, intervention and management. Rarely is the term defined in the literature and as a concept it is multifaceted. Concept analysis is a method for exploring and evaluating the meaning of words. It gives precise definitions, both theoretical and operational, for use in theory, clinical practice and research. A concept analysis provides a logical basis for defining terms through providing defining attributes, case examples (model, contrary, borderline, related), antecedents and consequences and the implications for nursing. Concept analysis helps us to refine and define a concept that derives from practice, research or theory. This paper will use the strategy of concept analysis to find a working definition for the concept of forensic risk. In conclusion, the historical background and literature are reviewed using concept analysis to bring the term into focus and to define it more clearly. Forensic risk is found to derive both from forensic practice and from risk theory. A proposed definition of forensic risk is given.

Chagas disease risk in Texas.

PubMed

Sarkar, Sahotra; Strutz, Stavana E; Frank, David M; Rivaldi, Chissa-Louise; Sissel, Blake; Sánchez-Cordero, Victor

2010-10-05

Chagas disease, caused by Trypanosoma cruzi, remains a serious public health concern in many areas of Latin America, including México. It is also endemic in Texas with an autochthonous canine cycle, abundant vectors (Triatoma species) in many counties, and established domestic and peridomestic cycles which make competent reservoirs available throughout the state. Yet, Chagas disease is not reportable in Texas, blood donor screening is not mandatory, and the serological profiles of human and canine populations remain unknown. The purpose of this analysis was to provide a formal risk assessment, including risk maps, which recommends the removal of these lacunae. The spatial relative risk of the establishment of autochthonous Chagas disease cycles in Texas was assessed using a five-stage analysis. 1. Ecological risk for Chagas disease was established at a fine spatial resolution using a maximum entropy algorithm that takes as input occurrence points of vectors and environmental layers. The analysis was restricted to triatomine vector species for which new data were generated through field collection and through collation of post-1960 museum records in both México and the United States with sufficiently low georeferenced error to be admissible given the spatial resolution of the analysis (1 arc-minute). The new data extended the distribution of vector species to 10 new Texas counties. The models predicted that Triatoma gerstaeckeri has a large region of contiguous suitable habitat in the southern United States and México, T. lecticularia has a diffuse suitable habitat distribution along both coasts of the same region, and T. sanguisuga has a disjoint suitable habitat distribution along the coasts of the United States. The ecological risk is highest in south Texas. 2. Incidence-based relative risk was computed at the county level using the Bayesian Besag-York-Mollié model and post-1960 T. cruzi incidence data. This risk is concentrated in south Texas. 3. The ecological and incidence-based risks were analyzed together in a multi-criteria dominance analysis of all counties and those counties in which there were as yet no reports of parasite incidence. Both analyses picked out counties in south Texas as those at highest risk. 4. As an alternative to the multi-criteria analysis, the ecological and incidence-based risks were compounded in a multiplicative composite risk model. Counties in south Texas emerged as those with the highest risk. 5. Risk as the relative expected exposure rate was computed using a multiplicative model for the composite risk and a scaled population county map for Texas. Counties with highest risk were those in south Texas and a few counties with high human populations in north, east, and central Texas showing that, though Chagas disease risk is concentrated in south Texas, it is not restricted to it. For all of Texas, Chagas disease should be designated as reportable, as it is in Arizona and Massachusetts. At least for south Texas, lower than N, blood donor screening should be mandatory, and the serological profiles of human and canine populations should be established. It is also recommended that a joint initiative be undertaken by the United States and México to combat Chagas disease in the trans-border region. The methodology developed for this analysis can be easily exported to other geographical and disease contexts in which risk assessment is of potential value.

Development and Sensitivity Analysis of a Frost Risk model based primarily on freely distributed Earth Observation data

NASA Astrophysics Data System (ADS)

Louka, Panagiota; Petropoulos, George; Papanikolaou, Ioannis

2015-04-01

The ability to map the spatiotemporal distribution of extreme climatic conditions, such as frost, is a significant tool in successful agricultural management and decision making. Nowadays, with the development of Earth Observation (EO) technology, it is possible to obtain accurately, timely and in a cost-effective way information on the spatiotemporal distribution of frost conditions, particularly over large and otherwise inaccessible areas. The present study aimed at developing and evaluating a frost risk prediction model, exploiting primarily EO data from MODIS and ASTER sensors and ancillary ground observation data. For the evaluation of our model, a region in north-western Greece was selected as test site and a detailed sensitivity analysis was implemented. The agreement between the model predictions and the observed (remotely sensed) frost frequency obtained by MODIS sensor was evaluated thoroughly. Also, detailed comparisons of the model predictions were performed against reference frost ground observations acquired from the Greek Agricultural Insurance Organization (ELGA) over a period of 10-years (2000-2010). Overall, results evidenced the ability of the model to produce reasonably well the frost conditions, following largely explainable patterns in respect to the study site and local weather conditions characteristics. Implementation of our proposed frost risk model is based primarily on satellite imagery analysis provided nowadays globally at no cost. It is also straightforward and computationally inexpensive, requiring much less effort in comparison for example to field surveying. Finally, the method is adjustable to be potentially integrated with other high resolution data available from both commercial and non-commercial vendors. Keywords: Sensitivity analysis, frost risk mapping, GIS, remote sensing, MODIS, Greece

Approximate Uncertainty Modeling in Risk Analysis with Vine Copulas

PubMed Central

Bedford, Tim; Daneshkhah, Alireza

2015-01-01

Many applications of risk analysis require us to jointly model multiple uncertain quantities. Bayesian networks and copulas are two common approaches to modeling joint uncertainties with probability distributions. This article focuses on new methodologies for copulas by developing work of Cooke, Bedford, Kurowica, and others on vines as a way of constructing higher dimensional distributions that do not suffer from some of the restrictions of alternatives such as the multivariate Gaussian copula. The article provides a fundamental approximation result, demonstrating that we can approximate any density as closely as we like using vines. It further operationalizes this result by showing how minimum information copulas can be used to provide parametric classes of copulas that have such good levels of approximation. We extend previous approaches using vines by considering nonconstant conditional dependencies, which are particularly relevant in financial risk modeling. We discuss how such models may be quantified, in terms of expert judgment or by fitting data, and illustrate the approach by modeling two financial data sets. PMID:26332240

Risk Perception as the Quantitative Parameter of Ethics and Responsibility in Disaster Study

NASA Astrophysics Data System (ADS)

Kostyuchenko, Yuriy; Movchan, Dmytro

2014-05-01

Intensity of impacts of natural disasters is increasing with climate and ecological changes spread. Frequency of disasters is increasing, and recurrence of catastrophes characterizing by essential spatial heterogeneity. Distribution of losses is fundamentally non-linear and reflects complex interrelation of natural, social and environmental factor in the changing world on multi scale range. We faced with new types of risks, which require a comprehensive security concept. Modern understanding of complex security, and complex risk management require analysis of all natural and social phenomena, involvement of all available data, constructing of advanced analytical tools, and transformation of our perception of risk and security issues. Traditional deterministic models used for risk analysis are difficult applicable for analysis of social issues, as well as for analysis of multi scale multi-physics phenomena quantification. Also parametric methods are not absolutely effective because the system analyzed is essentially non-ergodic. The stochastic models of risk analysis are applicable for quantitative analysis of human behavior and risk perception. In framework of risk analysis models the risk perception issues were described. Risk is presented as the superposition of distribution (f(x,y)) and damage functions (p(x,y)): P →δΣ x,yf(x,y)p(x,y). As it was shown risk perception essentially influents to the damage function. Basing on the prospect theory and decision making under uncertainty on cognitive bias and handling of risk, modification of damage function is proposed: p(x,y|α(t)). Modified damage function includes an awareness function α(t), which is the system of risk perception function (rp) and function of education and log-term experience (c) as: α(t) → (c - rp). Education function c(t) describes the trend of education and experience. Risk perception function rp reflects security concept of human behavior, is the basis for prediction of socio-economic and socio-ecological processes. Also there is important positive feedback of risk perception function to distribution function. Risk perception is essentially depends of short-term recent events impact in multi agent media. This is managed function. The generalized view of awareness function is proposed: α(t) = δΣ ic - rpi. Using this form separate parameters has been calculated. For example, risk perception function is about 15-55% of awareness function depends of education, age and social status of people. Also it was estimated that fraction of awareness function in damage function, and so in function of risk is about 15-20%. It means that no less than 8-12% of direct losses depend of short-term responsible behavior of 'information agents': social activity of experts, scientists, correct discussions on ethical issues in geo-sciences and media. Other 6-9% of losses are connected with level of public and professional education. This area is also should be field of responsibility of geo-scientists.

Adversarial risk analysis with incomplete information: a level-k approach.

PubMed

Rothschild, Casey; McLay, Laura; Guikema, Seth

2012-07-01

This article proposes, develops, and illustrates the application of level-k game theory to adversarial risk analysis. Level-k reasoning, which assumes that players play strategically but have bounded rationality, is useful for operationalizing a Bayesian approach to adversarial risk analysis. It can be applied in a broad class of settings, including settings with asynchronous play and partial but incomplete revelation of early moves. Its computational and elicitation requirements are modest. We illustrate the approach with an application to a simple defend-attack model in which the defender's countermeasures are revealed with a probability less than one to the attacker before he decides on how or whether to attack. © 2011 Society for Risk Analysis.

The Relative Importance of the Vadose Zone in Multimedia Risk Assessment Modeling Applied at a National Scale: An Analysis of Benzene Using 3MRA

NASA Astrophysics Data System (ADS)

Babendreier, J. E.

2002-05-01

Evaluating uncertainty and parameter sensitivity in environmental models can be a difficult task, even for low-order, single-media constructs driven by a unique set of site-specific data. The challenge of examining ever more complex, integrated, higher-order models is a formidable one, particularly in regulatory settings applied on a national scale. Quantitative assessment of uncertainty and sensitivity within integrated, multimedia models that simulate hundreds of sites, spanning multiple geographical and ecological regions, will ultimately require a systematic, comparative approach coupled with sufficient computational power. The Multimedia, Multipathway, and Multireceptor Risk Assessment Model (3MRA) is an important code being developed by the United States Environmental Protection Agency for use in site-scale risk assessment (e.g. hazardous waste management facilities). The model currently entails over 700 variables, 185 of which are explicitly stochastic. The 3MRA can start with a chemical concentration in a waste management unit (WMU). It estimates the release and transport of the chemical throughout the environment, and predicts associated exposure and risk. The 3MRA simulates multimedia (air, water, soil, sediments), pollutant fate and transport, multipathway exposure routes (food ingestion, water ingestion, soil ingestion, air inhalation, etc.), multireceptor exposures (resident, gardener, farmer, fisher, ecological habitats and populations), and resulting risk (human cancer and non-cancer effects, ecological population and community effects). The 3MRA collates the output for an overall national risk assessment, offering a probabilistic strategy as a basis for regulatory decisions. To facilitate model execution of 3MRA for purposes of conducting uncertainty and sensitivity analysis, a PC-based supercomputer cluster was constructed. Design of SuperMUSE, a 125 GHz Windows-based Supercomputer for Model Uncertainty and Sensitivity Evaluation is described, along with the conceptual layout of an accompanying java-based paralleling software toolset. Preliminary work is also reported for a scenario involving Benzene disposal that describes the relative importance of the vadose zone in driving risk levels for ecological receptors and human health. Incorporating landfills, waste piles, aerated tanks, surface impoundments, and land application units, the site-based data used in the analysis included 201 national facilities representing 419 site-WMU combinations.

The association between COMT Val158Met polymorphism and migraine risk: A meta-analysis.

PubMed

Liao, Yao-Jun; Jiang, Jing-Ru; Jin, San-Qing

2017-05-01

Background The COMT Val158Met polymorphism has long been regarded as a risk factor for migraine. The possible association between COMT Val158Met polymorphism and migraine has been evaluated in several studies, but the results are not consistent. Therefore, we conduct this meta-analysis to address these issues. Methods The WEB OF SCIENCE and EMBASE databases were searched for eligible studies. The odds ratio (OR) with the corresponding 95% confidence interval (CI) was calculated to estimate the strength of the association between COMT Val158Met polymorphism and migraine. Results Five studies with 979 cases and 1870 controls were ultimately included in the present meta-analysis. The overall data showed no significant association between COMT Val158Met polymorphism and migraine in the multiplicative model (OR = 0.97, 95% CI: 0.78-1.21, p = 0.805) and dominant model (OR = 1.05, 95% CI: 0.75-1.48, p = 0.773), neither in the additive model (OR = 0.97, 95% CI: 0.77-1.23, p = 0.817) nor in the recessive model (OR = 0.88, 95% CI: 0.71-1.09, p = 0.246). In subgroup analysis, both for Caucasian and Asian populations, no statistically significant associations were observed in any genetic models. Conclusions Our meta-analysis suggested that the COMT Val158Met polymorphism was not associated with migraine risk.

Estimating the Value-at-Risk for some stocks at the capital market in Indonesia based on ARMA-FIGARCH models

NASA Astrophysics Data System (ADS)

Sukono; Lesmana, E.; Susanti, D.; Napitupulu, H.; Hidayat, Y.

2017-11-01

Value-at-Risk has already become a standard measurement that must be carried out by the financial institution for both internal interest and regulatory. In this paper, the estimation of Value-at-Risk of some stocks with econometric models approach is analyzed. In this research, we assume that the stock return follows the time series model. To do the estimation of mean value we are using ARMA models, while to estimate the variance value we are using FIGARCH models. Furthermore, the mean value estimator and the variance are used to estimate the Value-at-Risk. The result of the analysis shows that from five stock PRUF, BBRI, MPPA, BMRI, and INDF, the Value-at-Risk obtained are 0.01791, 0.06037, 0.02550, 0.06030, and 0.02585 respectively. Since Value-at-Risk represents the maximum risk size of each stock at a 95% level of significance, then it can be taken into consideration in determining the investment policy on stocks.

Score tests for independence in semiparametric competing risks models.

PubMed

Saïd, Mériem; Ghazzali, Nadia; Rivest, Louis-Paul

2009-12-01

A popular model for competing risks postulates the existence of a latent unobserved failure time for each risk. Assuming that these underlying failure times are independent is attractive since it allows standard statistical tools for right-censored lifetime data to be used in the analysis. This paper proposes simple independence score tests for the validity of this assumption when the individual risks are modeled using semiparametric proportional hazards regressions. It assumes that covariates are available, making the model identifiable. The score tests are derived for alternatives that specify that copulas are responsible for a possible dependency between the competing risks. The test statistics are constructed by adding to the partial likelihoods for the individual risks an explanatory variable for the dependency between the risks. A variance estimator is derived by writing the score function and the Fisher information matrix for the marginal models as stochastic integrals. Pitman efficiencies are used to compare test statistics. A simulation study and a numerical example illustrate the methodology proposed in this paper.

Review and comparison between the Wells-Riley and dose-response approaches to risk assessment of infectious respiratory diseases.

PubMed

Sze To, G N; Chao, C Y H

2010-02-01

Infection risk assessment is very useful in understanding the transmission dynamics of infectious diseases and in predicting the risk of these diseases to the public. Quantitative infection risk assessment can provide quantitative analysis of disease transmission and the effectiveness of infection control measures. The Wells-Riley model has been extensively used for quantitative infection risk assessment of respiratory infectious diseases in indoor premises. Some newer studies have also proposed the use of dose-response models for such purpose. This study reviews and compares these two approaches to infection risk assessment of respiratory infectious diseases. The Wells-Riley model allows quick assessment and does not require interspecies extrapolation of infectivity. Dose-response models can consider other disease transmission routes in addition to airborne route and can calculate the infectious source strength of an outbreak in terms of the quantity of the pathogen rather than a hypothetical unit. Spatial distribution of airborne pathogens is one of the most important factors in infection risk assessment of respiratory disease. Respiratory deposition of aerosol induces heterogeneous infectivity of intake pathogens and randomness on the intake dose, which are not being well accounted for in current risk models. Some suggestions for further development of the risk assessment models are proposed. This review article summarizes the strengths and limitations of the Wells-Riley and the dose-response models for risk assessment of respiratory diseases. Even with many efforts by various investigators to develop and modify the risk assessment models, some limitations still persist. This review serves as a reference for further development of infection risk assessment models of respiratory diseases. The Wells-Riley model and dose-response model offer specific advantages. Risk assessors can select the approach that is suitable to their particular conditions to perform risk assessment.

The use of cluster analysis techniques in spaceflight project cost risk estimation

NASA Technical Reports Server (NTRS)

Fox, G.; Ebbeler, D.; Jorgensen, E.

2003-01-01

Project cost risk is the uncertainty in final project cost, contingent on initial budget, requirements and schedule. For a proposed mission, a dynamic simulation model relying for some of its input on a simple risk elicitation is used to identify and quantify systemic cost risk.

School Health Promotion Policies and Adolescent Risk Behaviors in Israel: A Multilevel Analysis

ERIC Educational Resources Information Center

Tesler, Riki; Harel-Fisch, Yossi; Baron-Epel, Orna

2016-01-01

Background: Health promotion policies targeting risk-taking behaviors are being implemented across schools in Israel. This study identified the most effective components of these policies influencing cigarette smoking and alcohol consumption among adolescents. Methods: Logistic hierarchical linear model (HLM) analysis of data for 5279 students in…

A Healthy Dietary Pattern Reduces Lung Cancer Risk: A Systematic Review and Meta-Analysis.

PubMed

Sun, Yanlai; Li, Zhenxiang; Li, Jianning; Li, Zengjun; Han, Jianjun

2016-03-04

Diet and nutrients play an important role in cancer development and progress; a healthy dietary pattern has been found to be associated with several types of cancer. However, the association between a healthy eating pattern and lung cancer risk is still unclear. Therefore, we conducted a systematic review with meta-analysis to evaluate whether a healthy eating pattern might reduce lung cancer risk. We identified relevant studies from the PubMed and Embase databases up to October 2015, and the relative risks were extracted and combined by the fixed-effects model when no substantial heterogeneity was observed; otherwise, the random-effects model was employed. Subgroup and publication bias analyses were also performed. Finally, eight observational studies were included in the meta-analysis. The pooled relative risk of lung cancer for the highest vs. lowest category of healthy dietary pattern was 0.81 (95% confidence interval, CI: 0.75-0.86), and no significant heterogeneity was detected. The relative risks (RRs) for non-smokers, former smokers and current smokers were 0.89 (95% CI: 0.63-1.27), 0.74 (95% CI: 0.62-0.89) and 0.86 (95% CI: 0.79-0.93), respectively. The results remained stable in subgroup analyses by other confounders and sensitivity analysis. The results of our meta-analysis suggest that a healthy dietary pattern is associated with a lower lung cancer risk, and they provide more beneficial evidence for changing the diet pattern in the general population.

Maternal vitamin D status during pregnancy and risk of childhood asthma: A meta-analysis of prospective studies.

PubMed

Song, Huihui; Yang, Lei; Jia, Chongqi

2017-05-01

Mounting evidence suggests that maternal vitamin D status during pregnancy may be associated with development of childhood asthma, but the results are still inconsistent. A dose-response meta-analysis was performed to quantitatively summarize evidence on the association of maternal vitamin D status during pregnancy with the risk of childhood asthma. A systematic search was conducted to identify all studies assessing the association of maternal 25-hydroxyvitamin D (25(OH)D) during pregnancy with risk of childhood asthma. The fixed or random-effect model was selected based on the heterogeneity test among studies. Nonlinear dose-response relationship was assessed by restricted cubic spline model. Fifteen prospective studies with 12 758 participants and 1795 cases were included in the meta-analysis. The pooled relative risk of childhood asthma comparing the highest versus lowest category of maternal 25(OH)D levels was 0.87 (95% confidence interval, CI, 0.75-1.02). For dose-response analysis, evidence of a U-shaped relationship was found between maternal 25(OH)D levels and risk of childhood asthma (P nonlinearity = 0.02), with the lowest risk at approximately 70 nmol/L of 25(OH)D. This dose-response meta-analysis suggested a U-shaped relationship between maternal blood 25(OH)D levels and risk of childhood asthma. Further studies are needed to confirm the association. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bayesian dose-response analysis for epidemiological studies with complex uncertainty in dose estimation.

PubMed

Kwon, Deukwoo; Hoffman, F Owen; Moroz, Brian E; Simon, Steven L

2016-02-10

Most conventional risk analysis methods rely on a single best estimate of exposure per person, which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods. When the estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods and better coverage, that is, a markedly increased capability to include the true risk coefficient within the 95% credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure. Copyright © 2015 John Wiley & Sons, Ltd.

How can sensitivity analysis improve the robustness of mathematical models utilized by the re/insurance industry?

NASA Astrophysics Data System (ADS)

Noacco, V.; Wagener, T.; Pianosi, F.; Philp, T.

2017-12-01

Insurance companies provide insurance against a wide range of threats, such as natural catastrophes, nuclear incidents and terrorism. To quantify risk and support investment decisions, mathematical models are used, for example to set the premiums charged to clients that protect from financial loss, should deleterious events occur. While these models are essential tools for adequately assessing the risk attached to an insurer's portfolio, their development is costly and their value for decision-making may be limited by an incomplete understanding of uncertainty and sensitivity. Aside from the business need to understand risk and uncertainty, the insurance sector also faces regulation which requires them to test their models in such a way that uncertainties are appropriately captured and that plans are in place to assess the risks and their mitigation. The building and testing of models constitutes a high cost for insurance companies, and it is a time intensive activity. This study uses an established global sensitivity analysis toolbox (SAFE) to more efficiently capture the uncertainties and sensitivities embedded in models used by a leading re/insurance firm, with structured approaches to validate these models and test the impact of assumptions on the model predictions. It is hoped that this in turn will lead to better-informed and more robust business decisions.

HESI EXPOSURE FACTORS DATABASE FOR AGGREGATE AND CUMULATIVE RISK ASSESSMENT

EPA Science Inventory

In recent years, the risk analysis community has broadened its use of complex aggregate and cumulative residential exposure models (e.g., to meet the requirements of the 1996 Food Quality Protection Act). The value of these models is their ability to incorporate a range of inp...

A simulation model for risk assessment of turbine wheels

NASA Technical Reports Server (NTRS)

Safie, Fayssal M.; Hage, Richard T.

1991-01-01

A simulation model has been successfully developed to evaluate the risk of the Space Shuttle auxiliary power unit (APU) turbine wheels for a specific inspection policy. Besides being an effective tool for risk/reliability evaluation, the simulation model also allows the analyst to study the trade-offs between wheel reliability, wheel life, inspection interval, and rejection crack size. For example, in the APU application, sensitivity analysis results showed that the wheel life limit has the least effect on wheel reliability when compared to the effect of the inspection interval and the rejection crack size. In summary, the simulation model developed represents a flexible tool to predict turbine wheel reliability and study the risk under different inspection policies.

A simulation model for risk assessment of turbine wheels

NASA Astrophysics Data System (ADS)

Safie, Fayssal M.; Hage, Richard T.

A simulation model has been successfully developed to evaluate the risk of the Space Shuttle auxiliary power unit (APU) turbine wheels for a specific inspection policy. Besides being an effective tool for risk/reliability evaluation, the simulation model also allows the analyst to study the trade-offs between wheel reliability, wheel life, inspection interval, and rejection crack size. For example, in the APU application, sensitivity analysis results showed that the wheel life limit has the least effect on wheel reliability when compared to the effect of the inspection interval and the rejection crack size. In summary, the simulation model developed represents a flexible tool to predict turbine wheel reliability and study the risk under different inspection policies.

Factors accounting for youth suicide attempt in Hong Kong: a model building.

PubMed

Wan, Gloria W Y; Leung, Patrick W L

2010-10-01

This study aimed at proposing and testing a conceptual model of youth suicide attempt. We proposed a model that began with family factors such as a history of physical abuse and parental divorce/separation. Family relationship, presence of psychopathology, life stressors, and suicide ideation were postulated as mediators, leading to youth suicide attempt. The stepwise entry of the risk factors to a logistic regression model defined their proximity as related to suicide attempt. Path analysis further refined our proposed model of youth suicide attempt. Our originally proposed model was largely confirmed. The main revision was dropping parental divorce/separation as a risk factor in the model due to lack of significant contribution when examined alongside with other risk factors. This model was cross-validated by gender. This study moved research on youth suicide from identification of individual risk factors to model building, integrating separate findings of the past studies.

LinkIT: a ludic elicitation game for eliciting risk perceptions.

PubMed

Cao, Yan; McGill, William L

2013-06-01

The mental models approach, a leading strategy to develop risk communications, involves a time- and labor-intensive interview process and a lengthy questionnaire to elicit group-level risk perceptions. We propose that a similarity ratings approach for structural knowledge elicitation can be adopted to assist the risk mental models approach. The LinkIT game, inspired by games with a purpose (GWAP) technology, is a ludic elicitation tool designed to elicit group understanding of the relations between risk factors in a more enjoyable and productive manner when compared to traditional approaches. That is, consistent with the idea of ludic elicitation, LinkIT was designed to make the elicitation process fun and enjoyable in the hopes of increasing participation and data quality in risk studies. Like the mental models approach, the group mental model obtained via the LinkIT game can hence be generated and represented in a form of influence diagrams. In order to examine the external validity of LinkIT, we conducted a study to compare its performance with respect to a more conventional questionnaire-driven approach. Data analysis results conclude that the two group mental models elicited from the two approaches are similar to an extent. Yet, LinkIT was more productive and enjoyable than the questionnaire. However, participants commented that the current game has some usability concerns. This presentation summarizes the design and evaluation of the LinkIT game and suggests areas for future work. © 2012 Society for Risk Analysis.

Decision analysis and risk models for land development affecting infrastructure systems.

PubMed

Thekdi, Shital A; Lambert, James H

2012-07-01

Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential land development. © 2011 Society for Risk Analysis.

Applying the partitioned multiobjective risk method (PMRM) to portfolio selection.

PubMed

Reyes Santos, Joost; Haimes, Yacov Y

2004-06-01

The analysis of risk-return tradeoffs and their practical applications to portfolio analysis paved the way for Modern Portfolio Theory (MPT), which won Harry Markowitz a 1992 Nobel Prize in Economics. A typical approach in measuring a portfolio's expected return is based on the historical returns of the assets included in a portfolio. On the other hand, portfolio risk is usually measured using volatility, which is derived from the historical variance-covariance relationships among the portfolio assets. This article focuses on assessing portfolio risk, with emphasis on extreme risks. To date, volatility is a major measure of risk owing to its simplicity and validity for relatively small asset price fluctuations. Volatility is a justified measure for stable market performance, but it is weak in addressing portfolio risk under aberrant market fluctuations. Extreme market crashes such as that on October 19, 1987 ("Black Monday") and catastrophic events such as the terrorist attack of September 11, 2001 that led to a four-day suspension of trading on the New York Stock Exchange (NYSE) are a few examples where measuring risk via volatility can lead to inaccurate predictions. Thus, there is a need for a more robust metric of risk. By invoking the principles of the extreme-risk-analysis method through the partitioned multiobjective risk method (PMRM), this article contributes to the modeling of extreme risks in portfolio performance. A measure of an extreme portfolio risk, denoted by f(4), is defined as the conditional expectation for a lower-tail region of the distribution of the possible portfolio returns. This article presents a multiobjective problem formulation consisting of optimizing expected return and f(4), whose solution is determined using Evolver-a software that implements a genetic algorithm. Under business-as-usual market scenarios, the results of the proposed PMRM portfolio selection model are found to be compatible with those of the volatility-based model. However, under extremely unfavorable market conditions, results indicate that f(4) can be a more valid measure of risk than volatility.

The role of the RTEL1 rs2297440 polymorphism in the risk of glioma development: a meta-analysis.

PubMed

Zhang, Cuiping; Lu, Yu; Zhang, Xiaolian; Yang, Dongmei; Shang, Shuxin; Liu, Denghe; Jiang, Kongmei; Huang, Weiqiang

2016-07-01

The regulator of the telomere elongation helicase1 (RTEL1) gene plays a crucial role in the DNA double-stand break-repair pathway by maintaining genomic stability. Recent epidemiological studies showed that the rs2297440 polymorphism in the RTEL1 gene was a potential risk locus for glioma development, but the results were inconclusive. To clarify the association between this polymorphism and the risk of glioma, we performed a comprehensive meta-analysis. The PubMed, EMBASE, Web of Science, and China National Knowledge Infrastructure databases were systematically searched to identify all relevant published studies up to 30 August 2015. Four eligible studies were finally included. The pooled results indicated that the RTEL1 rs2297440 polymorphism moderately increased the risk of glioma in all genetic models. A comparison of the dominant model CT + CC versus TT (OR 1.40; 95 % CI 1.24-1.60; p < 0.001) indicated that having the C allele conferred a 40 % increased risk of developing glioma. In a subgroup analysis based on geographic location (Europe, Asia, and America), there was an association between the rs2297440 polymorphism and the risk of glioma in all three areas. The results of the subgroup analysis based on source of control indicated an elevated risk of glioma in population-based control studies. This meta-analysis demonstrates that the RTEL1 rs2297440 polymorphism plays a moderate, but significant role in the risk of glioma. Further studies with larger sample sizes are necessary to confirm this finding.

Predicting medical complications after spine surgery: a validated model using a prospective surgical registry.

PubMed

Lee, Michael J; Cizik, Amy M; Hamilton, Deven; Chapman, Jens R

2014-02-01

The possibility and likelihood of a postoperative medical complication after spine surgery undoubtedly play a major role in the decision making of the surgeon and patient alike. Although prior study has determined relative risk and odds ratio values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of medical complication, rather than relative risk or odds ratio values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. The purpose of this study was to create and validate a predictive model for the risk of medical complication during and after spine surgery. Statistical analysis using a prospective surgical spine registry that recorded extensive demographic, surgical, and complication data. Outcomes examined are medical complications that were specifically defined a priori. This analysis is a continuation of statistical analysis of our previously published report. Using a prospectively collected surgical registry of more than 1,476 patients with extensive demographic, comorbidity, surgical, and complication detail recorded for 2 years after surgery, we previously identified several risk factor for medical complications. Using the beta coefficients from those log binomial regression analyses, we created a model to predict the occurrence of medical complication after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created two predictive models: one predicting the occurrence of any medical complication and the other predicting the occurrence of a major medical complication. The final predictive model for any medical complications had a receiver operator curve characteristic of 0.76, considered to be a fair measure. The final predictive model for any major medical complications had receiver operator curve characteristic of 0.81, considered to be a good measure. The final model has been uploaded for use on SpineSage.com. We present a validated model for predicting medical complications after spine surgery. The value in this model is that it gives the user an absolute percent likelihood of complication after spine surgery based on the patient's comorbidity profile and invasiveness of surgery. Patients are far more likely to understand an absolute percentage, rather than relative risk and confidence interval values. A model such as this is of paramount importance in counseling patients and enhancing the safety of spine surgery. In addition, a tool such as this can be of great use particularly as health care trends toward pay-for-performance, quality metrics, and risk adjustment. To facilitate the use of this model, we have created a website (SpineSage.com) where users can enter in patient data to determine likelihood of medical complications after spine surgery. Copyright © 2014 Elsevier Inc. All rights reserved.

Seawater intrusion risk analysis under climate change conditions for the Gaza Strip aquifer (Palestine)

NASA Astrophysics Data System (ADS)

Dentoni, Marta; Deidda, Roberto; Paniconi, Claudio; Marrocu, Marino; Lecca, Giuditta

2014-05-01

Seawater intrusion (SWI) has become a major threat to coastal freshwater resources, particularly in the Mediterranean basin, where this problem is exacerbated by the lack of appropriate groundwater resources management and with serious potential impacts from projected climate changes. A proper analysis and risk assessment that includes climate scenarios is essential for the design of water management measures to mitigate the environmental and socio-economic impacts of SWI. In this study a methodology for SWI risk analysis in coastal aquifers is developed and applied to the Gaza Strip coastal aquifer in Palestine. The method is based on the origin-pathway-target model, evaluating the final value of SWI risk by applying the overlay principle to the hazard map (representing the origin of SWI), the vulnerability map (representing the pathway of groundwater flow) and the elements map (representing the target of SWI). Results indicate the important role of groundwater simulation in SWI risk assessment and illustrate how mitigation measures can be developed according to predefined criteria to arrive at quantifiable expected benefits. Keywords: Climate change, coastal aquifer, seawater intrusion, risk analysis, simulation/optimization model. Acknowledgements. The study is partially funded by the project "Climate Induced Changes on the Hydrology of Mediterranean Basins (CLIMB)", FP7-ENV-2009-1, GA 244151.

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

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

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

1993-09-01

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

Linking stressors and ecological responses

USGS Publications Warehouse

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

1999-01-01

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

Gambler Risk Perception: A Mental Model and Grounded Theory Analysis.

PubMed

Spurrier, Michael; Blaszczynski, Alexander; Rhodes, Paul

2015-09-01

Few studies have investigated how gamblers perceive risk or the role of risk perception in disordered gambling. The purpose of the current study therefore was to obtain data on lay gamblers' beliefs on these variables and their effects on decision-making, behaviour, and disordered gambling aetiology. Fifteen regular lay gamblers (non-problem/low risk, moderate risk and problem gamblers) completed a semi-structured interview following mental models and grounded theory methodologies. Gambler interview data was compared to an expert 'map' of risk-perception, to identify comparative gaps or differences associated with harmful or safe gambling. Systematic overlapping processes of data gathering and analysis were used to iteratively extend, saturate, test for exception, and verify concepts and themes emerging from the data. The preliminary findings suggested that gambler accounts supported the presence of expert conceptual constructs, and to some degree the role of risk perception in protecting against or increasing vulnerability to harm and disordered gambling. Gambler accounts of causality, meaning, motivation, and strategy were highly idiosyncratic, and often contained content inconsistent with measures of disordered gambling. Disordered gambling appears heavily influenced by relative underestimation of risk and overvaluation of gambling, based on explicit and implicit analysis, and deliberate, innate, contextual, and learned processing evaluations and biases.

A risk prediction score model for predicting occurrence of post-PCI vasovagal reflex syndrome: a single center study in Chinese population.

PubMed

Li, Hai-Yan; Guo, Yu-Tao; Tian, Cui; Song, Chao-Qun; Mu, Yang; Li, Yang; Chen, Yun-Dai

2017-08-01

The vasovagal reflex syndrome (VVRS) is common in the patients undergoing percutaneous coronary intervention (PCI). However, prediction and prevention of the risk for the VVRS have not been completely fulfilled. This study was conducted to develop a Risk Prediction Score Model to identify the determinants of VVRS in a large Chinese population cohort receiving PCI. From the hospital electronic medical database, we identified 3550 patients who received PCI (78.0% males, mean age 60 years) in Chinese PLA General Hospital from January 1, 2000 to August 30, 2016. The multivariate analysis and receiver operating characteristic (ROC) analysis were performed. The adverse events of VVRS in the patients were significantly increased after PCI procedure than before the operation (all P < 0.001). The rate of VVRS [95% confidence interval (CI)] in patients receiving PCI was 4.5% (4.1%-5.6%). Compared to the patients suffering no VVRS, incidence of VVRS involved the following factors, namely female gender, primary PCI, hypertension, over two stents implantation in the left anterior descending (LAD), and the femoral puncture site. The multivariate analysis suggested that they were independent risk factors for predicting the incidence of VVRS (all P < 0.001). We developed a risk prediction score model for VVRS. ROC analysis showed that the risk prediction score model was effectively predictive of the incidence of VVRS in patients receiving PCI (c-statistic 0.76, 95% CI: 0.72-0.79, P < 0.001). There were decreased events of VVRS in the patients receiving PCI whose diastolic blood pressure dropped by more than 30 mmHg and heart rate reduced by 10 times per minute (AUC: 0.84, 95% CI: 0.81-0.87, P < 0.001). The risk prediction score is quite efficient in predicting the incidence of VVRS in patients receiving PCI. In which, the following factors may be involved, the femoral puncture site, female gender, hypertension, primary PCI, and over 2 stents implanted in LAD.

Multisensor satellite data for water quality analysis and water pollution risk assessment: decision making under deep uncertainty with fuzzy algorithm in framework of multimodel approach

NASA Astrophysics Data System (ADS)

Kostyuchenko, Yuriy V.; Sztoyka, Yulia; Kopachevsky, Ivan; Artemenko, Igor; Yuschenko, Maxim

2017-10-01

Multi-model approach for remote sensing data processing and interpretation is described. The problem of satellite data utilization in multi-modeling approach for socio-ecological risks assessment is formally defined. Observation, measurement and modeling data utilization method in the framework of multi-model approach is described. Methodology and models of risk assessment in framework of decision support approach are defined and described. Method of water quality assessment using satellite observation data is described. Method is based on analysis of spectral reflectance of aquifers. Spectral signatures of freshwater bodies and offshores are analyzed. Correlations between spectral reflectance, pollutions and selected water quality parameters are analyzed and quantified. Data of MODIS, MISR, AIRS and Landsat sensors received in 2002-2014 have been utilized verified by in-field spectrometry and lab measurements. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality category is making based on fuzzy algorithm using limited set of uncertain parameters. Data from satellite observations, field measurements and modeling is utilizing in the framework of the approach proposed. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Problems of construction of spatial and temporal distribution of calculated parameters, as well as a problem of data regularization are discussed. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated and discussed.

The development and testing of a skin tear risk assessment tool.

PubMed

Newall, Nelly; Lewin, Gill F; Bulsara, Max K; Carville, Keryln J; Leslie, Gavin D; Roberts, Pam A

2017-02-01

The aim of the present study is to develop a reliable and valid skin tear risk assessment tool. The six characteristics identified in a previous case control study as constituting the best risk model for skin tear development were used to construct a risk assessment tool. The ability of the tool to predict skin tear development was then tested in a prospective study. Between August 2012 and September 2013, 1466 tertiary hospital patients were assessed at admission and followed up for 10 days to see if they developed a skin tear. The predictive validity of the tool was assessed using receiver operating characteristic (ROC) analysis. When the tool was found not to have performed as well as hoped, secondary analyses were performed to determine whether a potentially better performing risk model could be identified. The tool was found to have high sensitivity but low specificity and therefore have inadequate predictive validity. Secondary analysis of the combined data from this and the previous case control study identified an alternative better performing risk model. The tool developed and tested in this study was found to have inadequate predictive validity. The predictive validity of an alternative, more parsimonious model now needs to be tested. © 2015 Medicalhelplines.com Inc and John Wiley & Sons Ltd.

Dietary fiber intake reduces risk of inflammatory bowel disease: result from a meta-analysis.

PubMed

Liu, Xiaoqin; Wu, Yili; Li, Fang; Zhang, Dongfeng

2015-09-01

Several epidemiological investigations have been conducted to evaluate the relationship between dietary fiber intake and inflammatory bowel diseases, but the results are inconsistent. This meta-analysis was performed to quantitatively summarize the evidence from observational studies. PubMed, Embase, and Web of Knowledge were searched for relevant articles published up to November 2014. The combined relative risks were calculated with the fixed- or random-effects model. Dose-response relationship was assessed using restricted cubic spline model. We hypothesized that the meta-analysis could yield a summary effect, which would indicate that dietary fiber intake could decrease the risk of ulcerative colitis and Crohn disease (CD). Overall, 8 articles involving 2 cohort studies, 1 nested case-control study, and 5 case-control studies were finally included in this study. The pooled relative risks with 95% confidence intervals of ulcerative colitis and CD for the highest vs lowest categories of dietary fiber intake were 0.80 (0.64-1.00) and 0.44 (0.29-0.69), respectively. A linear dose-response relationship was found between dietary fiber and CD risk, and the risk of CD decreased by 13% (P < .05) for every 10 g/d increment in fiber intake. The results from this meta-analysis indicated that the intake of dietary fiber was significantly associated with a decreased risk of inflammatory bowel disease. Copyright © 2015 Elsevier Inc. All rights reserved.

Cytotoxic T-lymphocyte-associated protein 4 +49A/G polymorphisms contribute to the risk of type 1 diabetes in children: An updated systematic review and meta-analysis with trial sequential analysis.

PubMed

Wang, Bo; Du, Wei; Jia, Yutao; Zhang, Xiaobai; Ma, Guorui

2017-02-07

Type 1 diabetes (T1D) is a heritable disease associated with multiple genetic variants. This systematic review and meta-analysis assessed the correlation between cytotoxic T-lymphocyte-associated protein 4(CTLA-4) +49A/G polymorphisms and the risk of T1D in children. The random effects model was used to estimate the related odds ratios (ORs) and 95% confidence intervals (CIs). Trial sequential analysis (TSA) was used to determine whether the currently available evidence was sufficient and conclusive. Our results indicated that CTLA-4 gene polymorphisms significantly increased the risk of childhood T1D in an allelic model (G vs. A: OR=1.33, 95%CI=1.19-1.48; I2=44.0% and P=0.001for heterogeneity) and a codominant model (GG vs. AA: OR=1.75, 95%CI=1.37-2.24; I2=57.5% and P=0.001for heterogeneity; GA vs. AA: OR=1.26, 95%CI=1.09-1.46; I2=40.4% and P=0.036for heterogeneity). Subgroup analysis results indicated that the ORs were higher in the Asian population (ORallelic model=1.60, ORGG vs. AA=2.46 and ORGA vs. AA=1.58) than the Caucasian population (ORallelic model==1.24, ORGG vs. AA=1.55 and ORGA vs. AA=1.19). The TSA results indicated that the evidence of the effect was sufficient. In conclusion, CTLA4 +49A/G polymorphisms increased the risk of T1D in children, and CTLA4 +49A/G can be considered to be a genetic marker for T1D in children.

Relationships between mastitis and functional longevity in Danish Black and White dairy cattle estimated using survival analysis.

PubMed

Neerhof, H J; Madsen, P; Ducrocq, V P; Vollema, A R; Jensen, J; Korsgaard, I R

2000-05-01

The relationship between mastitis and functional longevity was assessed with survival analysis on data of Danish Black and White dairy cows. Different methods of including the effect of mastitis treatment on the culling decision by a farmer in the model were compared. The model in which mastitis treatment was assumed to have an effect on functional longevity until the end of the lactation had the highest likelihood, and the model in which mastitis treatment had an effect for only a short period had the lowest likelihood. A cow with mastitis had 1.69 times greater risk of being culled than did a healthy herdmate with all other effects being the same. A model without mastitis treatment was used to predict transmitting abilities of bulls for risk of being culled, based on longevity records of their daughters, and was expressed in terms of risk of being culled. The correlation between the risk of being culled and the national evaluations of the bulls for mastitis resistance was approximately -0.4, indicating that resistance against mastitis was genetically correlated with a lower risk of being culled and, thus, a longer functional length of productive life.

Characterizing the performance of the Conway-Maxwell Poisson generalized linear model.

PubMed

Francis, Royce A; Geedipally, Srinivas Reddy; Guikema, Seth D; Dhavala, Soma Sekhar; Lord, Dominique; LaRocca, Sarah

2012-01-01

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway-Maxwell Poisson (COM-Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM-Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM-Poisson GLM, and (2) estimate the prediction accuracy of the COM-Poisson GLM using simulated data sets. The results of the study indicate that the COM-Poisson GLM is flexible enough to model under-, equi-, and overdispersed data sets with different sample mean values. The results also show that the COM-Poisson GLM yields accurate parameter estimates. The COM-Poisson GLM provides a promising and flexible approach for performing count data regression. © 2011 Society for Risk Analysis.

An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes.

PubMed

Shapira, Stav; Novack, Lena; Bar-Dayan, Yaron; Aharonson-Daniel, Limor

2016-01-01

A comprehensive technique for earthquake-related casualty estimation remains an unmet challenge. This study aims to integrate risk factors related to characteristics of the exposed population and to the built environment in order to improve communities' preparedness and response capabilities and to mitigate future consequences. An innovative model was formulated based on a widely used loss estimation model (HAZUS) by integrating four human-related risk factors (age, gender, physical disability and socioeconomic status) that were identified through a systematic review and meta-analysis of epidemiological data. The common effect measures of these factors were calculated and entered to the existing model's algorithm using logistic regression equations. Sensitivity analysis was performed by conducting a casualty estimation simulation in a high-vulnerability risk area in Israel. the integrated model outcomes indicated an increase in the total number of casualties compared with the prediction of the traditional model; with regard to specific injury levels an increase was demonstrated in the number of expected fatalities and in the severely and moderately injured, and a decrease was noted in the lightly injured. Urban areas with higher populations at risk rates were found more vulnerable in this regard. The proposed model offers a novel approach that allows quantification of the combined impact of human-related and structural factors on the results of earthquake casualty modelling. Investing efforts in reducing human vulnerability and increasing resilience prior to an occurrence of an earthquake could lead to a possible decrease in the expected number of casualties.

Multi-hazard risk analysis for management strategies

NASA Astrophysics Data System (ADS)

Kappes, M.; Keiler, M.; Bell, R.; Glade, T.

2009-04-01

Risk management is very often operating in a reactive way, responding to an event, instead of proactive starting with risk analysis and building up the whole process of risk evaluation, prevention, event management and regeneration. Since damage and losses from natural hazards raise continuously more and more studies, concepts (e.g. Switzerland or South Tyrol-Bolozano) and software packages (e.g. ARMAGEDOM, HAZUS or RiskScape) are developed to guide, standardize and facilitate the risk analysis. But these approaches focus on different aspects and are mostly closely adapted to the situation (legislation, organization of the administration, specific processes etc.) of the specific country or region. We propose in this study the development of a flexible methodology for multi-hazard risk analysis, identifying the stakeholders and their needs, processes and their characteristics, modeling approaches as well as incoherencies occurring by combining all these different aspects. Based on this concept a flexible software package will be established consisting of ArcGIS as central base and being complemented by various modules for hazard modeling, vulnerability assessment and risk calculation. Not all modules will be developed newly but taken from the current state-of-the-art and connected or integrated into ArcGIS. For this purpose two study sites, Valtellina in Italy and Bacelonnette in France, were chosen and the hazards types debris flows, rockfalls, landslides, avalanches and floods are planned to be included in the tool for a regional multi-hazard risk analysis. Since the central idea of this tool is its flexibility this will only be a first step, in the future further processes and scales can be included and the instrument thus adapted to any study site.

Reduction of spatial distribution of risk factors for transportation of contaminants released by coal mining activities.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-09-15

It is reported that water-energy nexus composes two of the biggest development and human health challenges. In the present study we presented a Risk Potential Index (RPI) model which encapsulates Source, Vector (Transport), and Target risks for forecasting surface water contamination. The main aim of the model is to identify critical surface water risk zones for an open cast mining environment, taking Jharia Coalfield, India as the study area. The model also helps in feasible sampling design. Based on spatial analysis various risk zones were successfully delineated. Monthly RPI distribution revealed that the risk of surface water contamination was highest during the monsoon months. Surface water samples were analysed to validate the model. A GIS based alternative management option was proposed to reduce surface water contamination risk and observed 96% and 86% decrease in the spatial distribution of very high risk areas for the months June and July respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Risk models for post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP): smoking and chronic liver disease are predictors of protection against PEP.

PubMed

DiMagno, Matthew J; Spaete, Joshua P; Ballard, Darren D; Wamsteker, Erik-Jan; Saini, Sameer D

2013-08-01

We investigated which variables independently associated with protection against or development of postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) and severity of PEP. Subsequently, we derived predictive risk models for PEP. In a case-control design, 6505 patients had 8264 ERCPs, 211 patients had PEP, and 22 patients had severe PEP. We randomly selected 348 non-PEP controls. We examined 7 established- and 9 investigational variables. In univariate analysis, 7 variables predicted PEP: younger age, female sex, suspected sphincter of Oddi dysfunction (SOD), pancreatic sphincterotomy, moderate-difficult cannulation (MDC), pancreatic stent placement, and lower Charlson score. Protective variables were current smoking, former drinking, diabetes, and chronic liver disease (CLD, biliary/transplant complications). Multivariate analysis identified seven independent variables for PEP, three protective (current smoking, CLD-biliary, CLD-transplant/hepatectomy complications) and 4 predictive (younger age, suspected SOD, pancreatic sphincterotomy, MDC). Pre- and post-ERCP risk models of 7 variables have a C-statistic of 0.74. Removing age (seventh variable) did not significantly affect the predictive value (C-statistic of 0.73) and reduced model complexity. Severity of PEP did not associate with any variables by multivariate analysis. By using the newly identified protective variables with 3 predictive variables, we derived 2 risk models with a higher predictive value for PEP compared to prior studies.

Construction of a pathological risk model of occult lymph node metastases for prognostication by semi-automated image analysis of tumor budding in early-stage oral squamous cell carcinoma

PubMed Central

Pedersen, Nicklas Juel; Jensen, David Hebbelstrup; Lelkaitis, Giedrius; Kiss, Katalin; Charabi, Birgitte; Specht, Lena; von Buchwald, Christian

2017-01-01

It is challenging to identify at diagnosis those patients with early oral squamous cell carcinoma (OSCC), who have a poor prognosis and those that have a high risk of harboring occult lymph node metastases. The aim of this study was to develop a standardized and objective digital scoring method to evaluate the predictive value of tumor budding. We developed a semi-automated image-analysis algorithm, Digital Tumor Bud Count (DTBC), to evaluate tumor budding. The algorithm was tested in 222 consecutive patients with early-stage OSCC and major endpoints were overall (OS) and progression free survival (PFS). We subsequently constructed and cross-validated a binary logistic regression model and evaluated its clinical utility by decision curve analysis. A high DTBC was an independent predictor of both poor OS and PFS in a multivariate Cox regression model. The logistic regression model was able to identify patients with occult lymph node metastases with an area under the curve (AUC) of 0.83 (95% CI: 0.78–0.89, P <0.001) and a 10-fold cross-validated AUC of 0.79. Compared to other known histopathological risk factors, the DTBC had a higher diagnostic accuracy. The proposed, novel risk model could be used as a guide to identify patients who would benefit from an up-front neck dissection. PMID:28212555

The relationship between the effect of pravastatin and risk factors for coronary heart disease in Japanese patients with hypercholesterolemia.

PubMed

Ishikawa, Toshitsugu; Mizuno, Kyoichi; Nakaya, Noriaki; Ohashi, Yasuo; Tajima, Naoko; Kushiro, Toshio; Teramoto, Tamio; Uchiyama, Shinichiro; Nakamura, Haruo

2008-10-01

Several epidemiologic studies in Japan have shown the risk factors for coronary heart disease (CHD) in the general population. The present analysis determined the risk factors for CHD in the MEGA Study, a large primary prevention trial with pravastatin in Japanese with hypercholesterolemia. The relationship between each baseline characteristic and the risk of CHD for the 5-year study period were evaluated using the Cox proportional hazard model. The multivariable predictors of CHD were sex, age, high-density lipoprotein-cholesterol (HDL-C), diabetes mellitus (DM), hypertension (HT), and history of smoking. Serum total and low-density lipoprotein-cholesterol were not independent risk factors for CHD in the current analysis. In addition, the effect of pravastatin was evaluated by subgroups in each risk factor using the interaction in a Cox model. Diet plus pravastatin treatment reduced CHD risk by 14-43% compared with diet alone, regardless of the presence or absence of risk factors. The risk factors for CHD were sex, age, DM, HT, smoking, and low HDL-C in the MEGA Study. The pravastatin treatment was effective for reducing the risk of CHD, regardless of the presence of risk factors.

Decision modeling for fire incident analysis

Treesearch

Donald G. MacGregor; Armando González-Cabán

2009-01-01

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

Return on Scientific Investment - RoSI: a PMO dynamical index proposal for scientific projects performance evaluation and management.

PubMed

Caous, Cristofer André; Machado, Birajara; Hors, Cora; Zeh, Andrea Kaufmann; Dias, Cleber Gustavo; Amaro Junior, Edson

2012-01-01

To propose a measure (index) of expected risks to evaluate and follow up the performance analysis of research projects involving financial and adequate structure parameters for its development. A ranking of acceptable results regarding research projects with complex variables was used as an index to gauge a project performance. In order to implement this method the ulcer index as the basic model to accommodate the following variables was applied: costs, high impact publication, fund raising, and patent registry. The proposed structured analysis, named here as RoSI (Return on Scientific Investment) comprises a pipeline of analysis to characterize the risk based on a modeling tool that comprises multiple variables interacting in semi-quantitatively environments. This method was tested with data from three different projects in our Institution (projects A, B and C). Different curves reflected the ulcer indexes identifying the project that may have a minor risk (project C) related to development and expected results according to initial or full investment. The results showed that this model contributes significantly to the analysis of risk and planning as well as to the definition of necessary investments that consider contingency actions with benefits to the different stakeholders: the investor or donor, the project manager and the researchers.

Measuring the impact of air pollution on respiratory infection risk in China.

PubMed

Tang, Sanyi; Yan, Qinling; Shi, Wei; Wang, Xia; Sun, Xiaodan; Yu, Pengbo; Wu, Jianhong; Xiao, Yanni

2018-01-01

China is now experiencing major public health challenges caused by air pollution. Few studies have quantified the dynamics of air pollution and its impact on the risk of respiratory infection. We conducted an integrated data analysis to quantify the association among air quality index (AQI), meteorological variables and respiratory infection risk in Shaanxi province of China in the period of November 15th, 2010 to November 14th, 2016. Our analysis illustrated a statistically significantly positive correlation between the number of influenza-like illness (ILI) cases and AQI, and the respiratory infection risk has increased progressively with increased AQI with a time lag of 0-3 days. We also developed mathematical models for the AQI trend and respiratory infection dynamics, incorporating AQI-dependent incidence and AQI-based behaviour change interventions. Our combined data and modelling analysis estimated the basic reproduction number for the respiratory infection during the studying period to be 2.4076, higher than the basic reproduction number of the 2009 pandemic influenza in the same province. Our modelling-based simulations concluded that, in terms of respiratory infection risk reduction, the persistent control of emission in the China's blue-sky programme is much more effective than substantial social-economic interventions implemented only during the smog days. Copyright © 2017 Elsevier Ltd. All rights reserved.

External validation of a published nomogram for prediction of brain metastasis in patients with extra-cerebral metastatic breast cancer and risk regression analysis.

PubMed

Genre, Ludivine; Roché, Henri; Varela, Léonel; Kanoun, Dorra; Ouali, Monia; Filleron, Thomas; Dalenc, Florence

2017-02-01

Survival of patients with metastatic breast cancer (MBC) suffering from brain metastasis (BM) is limited and this event is usually fatal. In 2010, the Graesslin's nomogram was published in order to predict subsequent BM in patients with breast cancer (BC) with extra-cerebral metastatic disease. This model aims to select a patient population at high risk for BM and thus will facilitate the design of prevention strategies and/or the impact of early treatment of BM in prospective clinical studies. Nomogram external validation was retrospectively applied to patients with BC and later BM between January 2005 and December 2012, treated in our institution. Moreover, risk factors of BM appearance were studied by Fine and Gray's competing risk analysis. Among 492 patients with MBC, 116 developed subsequent BM. Seventy of them were included for the nomogram validation. The discrimination is good (area under curve = 0.695 [95% confidence interval, 0.61-0.77]). Risk factors of BM appearance are: human epidermal growth factor receptor 2 (HER2) overexpression/amplification, triple-negative BC and number of extra-cerebral metastatic sites (>1). With a competing risk model, we highlight the nomogram interest for HER2+ tumour subgroup exclusively. Graesslin's nomogram external validation demonstrates exportability and reproducibility. Importantly, the competing risk model analysis provides additional information for the design of prospective trials concerning the early diagnosis of BM and/or preventive treatment on high risk patients with extra-cerebral metastatic BC. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Risk Assessment of Alzheimer's Disease using the Information Diffusion Model from Structural Magnetic Resonance Imaging.

PubMed

Beheshti, Iman; Olya, Hossain G T; Demirel, Hasan

2016-04-05

Recently, automatic risk assessment methods have been a target for the detection of Alzheimer's disease (AD) risk. This study aims to develop an automatic computer-aided AD diagnosis technique for risk assessment of AD using information diffusion theory. Information diffusion is a fuzzy mathematics logic of set-value that is used for risk assessment of natural phenomena, which attaches fuzziness (uncertainty) and incompleteness. Data were obtained from voxel-based morphometry analysis of structural magnetic resonance imaging. The information diffusion model results revealed that the risk of AD increases with a reduction of the normalized gray matter ratio (p > 0.5, normalized gray matter ratio <40%). The information diffusion model results were evaluated by calculation of the correlation of two traditional risk assessments of AD, the Mini-Mental State Examination and the Clinical Dementia Rating. The correlation results revealed that the information diffusion model findings were in line with Mini-Mental State Examination and Clinical Dementia Rating results. Application of information diffusion model contributes to the computerization of risk assessment of AD, which has a practical implication for the early detection of AD.

Lack of Association between Interleukin-10 Gene Polymorphisms and Graft Rejection Risk in Kidney Transplantation Recipients: A Meta-Analysis

PubMed Central

Xiong, Jiachuan; Wang, Yiqin; Zhang, Ying; Nie, Ling; Wang, Daihong; Huang, Yunjian; Feng, Bing; Zhang, Jingbo; Zhao, Jinghong

2015-01-01

Background Interleukin-10 (IL-10) is an important immunomodulatory cytokine. Several studies focused the association between IL-10 promoter gene polymorphisms and graft rejection risk in kidney transplantation recipients. However, the results of these studies remain inconclusive. The aim of this study was to conduct a meta-analysis to further assess the associations. Methods The PubMed, Embase, and Ovid Medline databases were searched. Two independent authors extracted data, and the effects were estimated from an odds ratio (OR) with 95% confidence intervals (CIs). Subgroup and sensitivity analyses identified sources of heterogeneity. Results A total of 16 studies including 595 rejection patients and 1239 stable graft patients were included in order to study the IL-10 -1082 (rs1800896 G/A), -819 (rs1800871 C/T), -592 (rs1800872 C/A) and IL-10 (-1082,-819,-592) polymorphisms. The -1082 G/A polymorphism was not associated with an increased graft rejection risk (OR = 1.03; 95%CI, 0.85–1.25, P = 0.74 for GA+AA vs. GG model). Moreover, all of the -819 C/T (OR = 1.06, 95%CI, 0.79–1.42, P = 0.70 for TA+TT vs. CC model), -592 C/A (OR = 1.10, 95% CI, 0.85–1.42, P = 0.47 for AC+AA vs. CC model) and IL-10 (-1082,-819,-592) polymorphisms (OR = 1.00, 95%CI, 0.79–1.27, P = 0.98 for I+L vs. H model) did not increase the graft rejection risk. In addition, we also performed subgroup analysis by ethnic group (mainly in Europeans or Asians) and rejection type (acute or chronic). There was also lack of evidence of a significant association between the IL-10 gene polymorphism and graft rejection risk. The present meta-analysis indicated that the IL-10 gene polymorphism was not associated with graft rejection risk in kidney transplantation recipients. Conclusion This meta-analysis found evidence that the IL-10 polymorphism does not increase the risk of graft rejection in kidney transplantation recipients. Further chronic rejection and other ethnic population studies are needed to confirm our results. PMID:26035439

Assessing risk factors for periodontitis using regression

NASA Astrophysics Data System (ADS)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

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

PubMed

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

2014-03-01

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

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.

Neyman, Markov processes and survival analysis.

PubMed

Yang, Grace

2013-07-01

J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.

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.

A simple rapid approach using coupled multivariate statistical methods, GIS and trajectory models to delineate areas of common oil spill risk

NASA Astrophysics Data System (ADS)

Guillen, George; Rainey, Gail; Morin, Michelle

2004-04-01

Currently, the Minerals Management Service uses the Oil Spill Risk Analysis model (OSRAM) to predict the movement of potential oil spills greater than 1000 bbl originating from offshore oil and gas facilities. OSRAM generates oil spill trajectories using meteorological and hydrological data input from either actual physical measurements or estimates generated from other hydrological models. OSRAM and many other models produce output matrices of average, maximum and minimum contact probabilities to specific landfall or target segments (columns) from oil spills at specific points (rows). Analysts and managers are often interested in identifying geographic areas or groups of facilities that pose similar risks to specific targets or groups of targets if a spill occurred. Unfortunately, due to the potentially large matrix generated by many spill models, this question is difficult to answer without the use of data reduction and visualization methods. In our study we utilized a multivariate statistical method called cluster analysis to group areas of similar risk based on potential distribution of landfall target trajectory probabilities. We also utilized ArcView™ GIS to display spill launch point groupings. The combination of GIS and multivariate statistical techniques in the post-processing of trajectory model output is a powerful tool for identifying and delineating areas of similar risk from multiple spill sources. We strongly encourage modelers, statistical and GIS software programmers to closely collaborate to produce a more seamless integration of these technologies and approaches to analyzing data. They are complimentary methods that strengthen the overall assessment of spill risks.

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

PubMed Central

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-01-01

Background Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. Methods In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Results Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Conclusion Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided. PMID:19036144

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers.

PubMed

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-11-26

Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.

Impact of covariate models on the assessment of the air pollution-mortality association in a single- and multipollutant context.

PubMed

Sacks, Jason D; Ito, Kazuhiko; Wilson, William E; Neas, Lucas M

2012-10-01

With the advent of multicity studies, uniform statistical approaches have been developed to examine air pollution-mortality associations across cities. To assess the sensitivity of the air pollution-mortality association to different model specifications in a single and multipollutant context, the authors applied various regression models developed in previous multicity time-series studies of air pollution and mortality to data from Philadelphia, Pennsylvania (May 1992-September 1995). Single-pollutant analyses used daily cardiovascular mortality, fine particulate matter (particles with an aerodynamic diameter ≤2.5 µm; PM(2.5)), speciated PM(2.5), and gaseous pollutant data, while multipollutant analyses used source factors identified through principal component analysis. In single-pollutant analyses, risk estimates were relatively consistent across models for most PM(2.5) components and gaseous pollutants. However, risk estimates were inconsistent for ozone in all-year and warm-season analyses. Principal component analysis yielded factors with species associated with traffic, crustal material, residual oil, and coal. Risk estimates for these factors exhibited less sensitivity to alternative regression models compared with single-pollutant models. Factors associated with traffic and crustal material showed consistently positive associations in the warm season, while the coal combustion factor showed consistently positive associations in the cold season. Overall, mortality risk estimates examined using a source-oriented approach yielded more stable and precise risk estimates, compared with single-pollutant analyses.

Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location-Specific Premiums in the Netherlands.

PubMed

Ermolieva, T; Filatova, T; Ermoliev, Y; Obersteiner, M; de Bruijn, K M; Jeuken, A

2017-01-01

As flood risks grow worldwide, a well-designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood-loss-sharing program involving private insurance based on location-specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS-based flood model and a stochastic optimization procedure with respect to location-specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile-related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures. © 2016 Society for Risk Analysis.

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--a new aspect of quality assessment in intensive care medicine: first results of an analysis of the DIVI's interdisciplinary quality assessment research group].

PubMed

Stiletto, R; Röthke, M; Schäfer, E; Lefering, R; Waydhas, Ch

2006-10-01

Patient security has become one of the major aspects of clinical management in recent years. The crucial point in research was focused on malpractice. In contradiction to the economic process in non medical fields, the analysis of errors during the in-patient treatment time was neglected. Patient risk management can be defined as a structured procedure in a clinical unit with the aim to reduce harmful events. A risk point model was created based on a Delphi process and founded on the DIVI data register. The risk point model was evaluated in clinically working ICU departments participating in the register data base. The results of the risk point evaluation will be integrated in the next data base update. This might be a step to improve the reliability of the register to measure quality assessment in the ICU.

Novel Threat-risk Index Using Probabilistic Risk Assessment and Human Reliability Analysis - Final Report

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

George A. Beitel

2004-02-01

In support of a national need to improve the current state-of-the-art in alerting decision makers to the risk of terrorist attack, a quantitative approach employing scientific and engineering concepts to develop a threat-risk index was undertaken at the Idaho National Engineering and Environmental Laboratory (INEEL). As a result of this effort, a set of models has been successfully integrated into a single comprehensive model known as Quantitative Threat-Risk Index Model (QTRIM), with the capability of computing a quantitative threat-risk index on a system level, as well as for the major components of the system. Such a threat-risk index could providemore » a quantitative variant or basis for either prioritizing security upgrades or updating the current qualitative national color-coded terrorist threat alert.« less

Carbon/graphite fiber risk analysis and assessment study: Assessment of risk to the Lockheed Model L-1011 commercial transport aircraft

NASA Technical Reports Server (NTRS)

Daniledes, J.; Koch, J. R.

1980-01-01

The risk associated with the accidental release of carbon/graphite fibers (CF) from fires on commercial transport aircraft incorporating composite materials was assessed. Data are developed to evaluate the potential for CF damage to electrical and electronic equipment, assess the cost risk, and evaluate the hazard to continued operation. The subjects covered include identification of susceptible equipments, determination of infiltration transfer functions, analysis of airport operations, calculation of probabilities of equipment failures, assessment of the cost risk, and evaluation of the hazard to continued operation. The results show the risks associated with CF contamination are negligible through 1993.

Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk.

PubMed

Fuster-Parra, P; Tauler, P; Bennasar-Veny, M; Ligęza, A; López-González, A A; Aguiló, A

2016-04-01

An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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

Wenzel, Tom

In this report we compare two measures of driver risks: fatality risk per vehicle registration-year, and casualty (fatality plus serious injury) risk per police-reported crash. Our analysis is based on three sets of data from five states (Florida, Illinois, Maryland, Missouri, and Pennsylvania): data on all police-reported crashes involving model year 2000 to 2004 vehicles; 2005 county-level vehicle registration data by vehicle model year and make/model; and odometer readings from vehicle emission inspection and maintenance (I/M) programs conducted in urban areas of four of the five states (Florida does not have an I/M program). The two measures of risk couldmore » differ for three reasons: casualty risks are different from fatality risk; risks per vehicle registration-year are different from risks per crash; and risks estimated from national data are different from risks from the five states analyzed here. We also examined the effect of driver behavior, crash location, and general vehicle design on risk, as well as sources of potential bias in using the crash data from five states.« less

Seismic Hazard Analysis — Quo vadis?

NASA Astrophysics Data System (ADS)

Klügel, Jens-Uwe

2008-05-01

The paper is dedicated to the review of methods of seismic hazard analysis currently in use, analyzing the strengths and weaknesses of different approaches. The review is performed from the perspective of a user of the results of seismic hazard analysis for different applications such as the design of critical and general (non-critical) civil infrastructures, technical and financial risk analysis. A set of criteria is developed for and applied to an objective assessment of the capabilities of different analysis methods. It is demonstrated that traditional probabilistic seismic hazard analysis (PSHA) methods have significant deficiencies, thus limiting their practical applications. These deficiencies have their roots in the use of inadequate probabilistic models and insufficient understanding of modern concepts of risk analysis, as have been revealed in some recent large scale studies. These deficiencies result in the lack of ability of a correct treatment of dependencies between physical parameters and finally, in an incorrect treatment of uncertainties. As a consequence, results of PSHA studies have been found to be unrealistic in comparison with empirical information from the real world. The attempt to compensate these problems by a systematic use of expert elicitation has, so far, not resulted in any improvement of the situation. It is also shown that scenario-earthquakes developed by disaggregation from the results of a traditional PSHA may not be conservative with respect to energy conservation and should not be used for the design of critical infrastructures without validation. Because the assessment of technical as well as of financial risks associated with potential damages of earthquakes need a risk analysis, current method is based on a probabilistic approach with its unsolved deficiencies. Traditional deterministic or scenario-based seismic hazard analysis methods provide a reliable and in general robust design basis for applications such as the design of critical infrastructures, especially with systematic sensitivity analyses based on validated phenomenological models. Deterministic seismic hazard analysis incorporates uncertainties in the safety factors. These factors are derived from experience as well as from expert judgment. Deterministic methods associated with high safety factors may lead to too conservative results, especially if applied for generally short-lived civil structures. Scenarios used in deterministic seismic hazard analysis have a clear physical basis. They are related to seismic sources discovered by geological, geomorphologic, geodetic and seismological investigations or derived from historical references. Scenario-based methods can be expanded for risk analysis applications with an extended data analysis providing the frequency of seismic events. Such an extension provides a better informed risk model that is suitable for risk-informed decision making.

A methodology for evacuation design for urban areas: theoretical aspects and experimentation

NASA Astrophysics Data System (ADS)

Russo, F.; Vitetta, A.

2009-04-01

This paper proposes an unifying approach for the simulation and design of a transportation system under conditions of incoming safety and/or security. Safety and security are concerned with threats generated by very different factors and which, in turn, generate emergency conditions, such as the 9/11, Madrid and London attacks, the Asian tsunami, and the Katrina hurricane; just considering the last five years. In transportation systems, when exogenous events happen and there is a sufficient interval time between the instant when the event happens and the instant when the event has effect on the population, it is possible to reduce the negative effects with the population evacuation. For this event in every case it is possible to prepare with short and long term the evacuation. For other event it is possible also to plan the real time evacuation inside the general risk methodology. The development of models for emergency conditions in transportation systems has not received much attention in the literature. The main findings in this area are limited to only a few public research centres and private companies. In general, there is no systematic analysis of the risk theory applied in the transportation system. Very often, in practice, the vulnerability and exposure in the transportation system are considered as similar variables, or in other worse cases the exposure variables are treated as vulnerability variables. Models and algorithms specified and calibrated in ordinary conditions cannot be directly applied in emergency conditions under the usual hypothesis considered. This paper is developed with the following main objectives: (a) to formalize the risk problem with clear diversification (for the consequences) in the definition of the vulnerability and exposure in a transportation system; thus the book offers improvements over consolidated quantitative risk analysis models, especially transportation risk analysis models (risk assessment); (b) to formalize a system of models for evacuation simulation; (c) to calibrate and validate system of model for evacuation simulation from a real experimentation. In relation to the proposed objectives in this paper: (a) a general framework about risk analysis is reported in the first part, with specific methods and models to analyze urban transportation system performances in emergency conditions when exogenous phenomena occur and for the specification of the risk function; (b) a formulation of the general evacuation problem in the standard simulation context of "what if" approach is specified in the second part with reference to the model considered for the simulation of transportation system in ordinary condition; (c) a set of models specified in the second part are calibrated and validated from a real experimentation in the third part. The experimentation was developed in the central business district of an Italian village and about 1000 inhabitants were evacuated, in order to construct a complete data-base. Our experiment required that socioeconomic information (population, number employed, public buildings, schools, etc.) and ‎transport supply characteristics (infrastructures, etc.) be measured before and during experimentation. The real data of evacuation were recorded with 30 video cameras for laboratory analysis. The results are divided into six strictly connected tasks: Demand models; Supply and supply-demand interaction models for users; Simulation of refuge areas for users; Design of path choice models for emergency vehicles; Pedestrian outflow models in a building; Planning process and guidelines.

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.

Astronaut Risk Levels During Crew Module (CM) Land Landing

NASA Technical Reports Server (NTRS)

Lawrence, Charles; Carney, Kelly S.; Littell, Justin

2007-01-01

The NASA Engineering Safety Center (NESC) is investigating the merits of water and land landings for the crew exploration vehicle (CEV). The merits of these two options are being studied in terms of cost and risk to the astronauts, vehicle, support personnel, and general public. The objective of the present work is to determine the astronaut dynamic response index (DRI), which measures injury risks. Risks are determined for a range of vertical and horizontal landing velocities. A structural model of the crew module (CM) is developed and computational simulations are performed using a transient dynamic simulation analysis code (LS-DYNA) to determine acceleration profiles. Landing acceleration profiles are input in a human factors model that determines astronaut risk levels. Details of the modeling approach, the resulting accelerations, and astronaut risk levels are provided.

Effects of Pro12Ala polymorphism in peroxisome proliferator-activated receptor-γ2 gene on metabolic syndrome risk: a meta-analysis.

PubMed

Zhang, Ruyi; Wang, Jiao; Yang, Rui; Sun, Jia; Chen, Rongping; Luo, Haizhao; Liu, Duan; Cai, Dehong

2014-02-01

Associations between peroxisome proliferator-activated receptor γ2 (PPARγ2) gene polymorphism and metabolic syndrome risk remained controversial and ambiguous. Thus, we performed a meta-analysis to assess the association between Pro12Ala polymorphism in PPARγ2 gene and metabolic syndrome susceptibility. An electronic literature search was conducted on Medline, OVID, Cochrane Library database, and the China National Knowledge Internet up to March 2013. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to calculate the strength of association in the fixed or random effects model. Ten studies involving a total of 4456 cases and 10343 controls were included in this meta-analysis. No statistical evidence of association was found between Pro12Ala polymorphism and metabolic syndrome risk in all genetic models (homozygote model: OR=0.83, 95% CI=0.62-1.12; heterozygote model: OR=1.04, 95% CI=0.94-1.14; dominant model: OR=1.02, 95% CI=0.93-1.12; recessive model: OR=0.83, 95% CI=0.62-1.11). No statistical evidence of significant association was observed when stratified by ethnicity, definition of metabolic syndrome, source of control groups and quality score of the selected articles. All in all, the results did not support a major role of the Pro12Ala variant of the PPARγ2 gene in metabolic syndrome risk. This meta-analysis suggested that the effect of Pro12Ala polymorphism in PPARγ2 gene may not be related to metabolic syndrome as an entity. However, Pro12Ala may affect the single component of metabolic syndrome. A large, well designed study is required to more adequately assess the role for Pro12Ala polymorphism on metabolic syndrome. © 2013 Elsevier B.V. All rights reserved.

Risk assessment of water pollution sources based on an integrated k-means clustering and set pair analysis method in the region of Shiyan, China.

PubMed

Li, Chunhui; Sun, Lian; Jia, Junxiang; Cai, Yanpeng; Wang, Xuan

2016-07-01

Source water areas are facing many potential water pollution risks. Risk assessment is an effective method to evaluate such risks. In this paper an integrated model based on k-means clustering analysis and set pair analysis was established aiming at evaluating the risks associated with water pollution in source water areas, in which the weights of indicators were determined through the entropy weight method. Then the proposed model was applied to assess water pollution risks in the region of Shiyan in which China's key source water area Danjiangkou Reservoir for the water source of the middle route of South-to-North Water Diversion Project is located. The results showed that eleven sources with relative high risk value were identified. At the regional scale, Shiyan City and Danjiangkou City would have a high risk value in term of the industrial discharge. Comparatively, Danjiangkou City and Yunxian County would have a high risk value in terms of agricultural pollution. Overall, the risk values of north regions close to the main stream and reservoir of the region of Shiyan were higher than that in the south. The results of risk level indicated that five sources were in lower risk level (i.e., level II), two in moderate risk level (i.e., level III), one in higher risk level (i.e., level IV) and three in highest risk level (i.e., level V). Also risks of industrial discharge are higher than that of the agricultural sector. It is thus essential to manage the pillar industry of the region of Shiyan and certain agricultural companies in the vicinity of the reservoir to reduce water pollution risks of source water areas. Copyright © 2016 Elsevier B.V. All rights reserved.

Red and processed meat consumption and the risk of lung cancer: a dose-response meta-analysis of 33 published studies

PubMed Central

Xue, Xiu-Juan; Gao, Qing; Qiao, Jian-Hong; Zhang, Jie; Xu, Cui-Ping; Liu, Ju

2014-01-01

This meta-analysis was to summarize the published studies about the association between red/processed meat consumption and the risk of lung cancer. 5 databases were systematically reviewed, and random-effect model was used to pool the study results and to assess dose-response relationships. Results shown that six cohort studies and twenty eight case-control studies were included in this meat-analysis. The pooled Risk Radios (RR) for total red meat and processed meat were 1.44 (95% CI, 1.29-1.61) and 1.23 (95% CI, 1.10-1.37), respectively. Dose-response analysis revealed that for every increment of 120 grams red meat per day the risk of lung cancer increases 35% and for every increment of 50 grams red meat per day the risk of lung cancer increases 20%. The present dose-response meta-analysis suggested that both red and processed meat consumption showed a positive effect on lung cancer risk. PMID:25035778

Assessment of NHTSA’s Report “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2004-2011 Passenger Cars and LTVs” (LBNL Phase 1)

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

Wenzel, Tom P.

In its 2012 report NHTSA simulated the effect four fleetwide mass reduction scenarios would have on the change in annual fatalities. NHTSA estimated that the most aggressive of these scenarios (reducing mass 5.2% in heavier light trucks and 2.6% in all other vehicles types except lighter cars) would result in a small reduction in societal fatalities. LBNL replicated the methodology NHTSA used to simulate six mass reduction scenarios, including the mass reductions recommended in the 2015 NRC committee report, and estimated in 2021 and 2025 by EPA in the TAR, using the updated data through 2012. The analysis indicates thatmore » the estimated x change in fatalities under each scenario based on the updated analysis is comparable to that in the 2012 analysis, but less beneficial or more detrimental than that in the 2016 analysis. For example, an across the board 100-lb reduction in mass would result in an estimated 157 additional annual fatalities based on the 2012 analysis, but would result in only an estimated 91 additional annual fatalities based on the 2016 analysis, and an additional 87 fatalities based on the current analysis. The mass reductions recommended by the 2015 NRC committee report6 would result in a 224 increase in annual fatalities in the 2012 analysis, a 344 decrease in annual fatalities in the 2016 analysis, and a 141 increase in fatalities in the current analysis. The mass reductions EPA estimated for 2025 in the TAR7 would result in a 203 decrease in fatalities based on the 2016 analysis, but an increase of 39 fatalities based on the current analysis. These results support NHTSA’s conclusion from its 2012 study that, when footprint is held fixed, “no judicious combination of mass reductions in the various classes of vehicles results in a statistically significant fatality increase and many potential combinations are safety-neutral as point estimates.”Like the previous NHTSA studies, this updated report concludes that the estimated effect of mass reduction while maintaining footprint on societal U.S. fatality risk is small, and not statistically significant at the 95% or 90% confidence level for all vehicle types based on the jack-knife method NHTSA used. This report also finds that the estimated effects of other control variables, such as vehicle type, specific safety technologies, and crash conditions such as whether the crash occurred at night, in a rural county, or on a high-speed road, on risk are much larger, in some cases two orders of magnitude larger, than the estimated effect of mass or footprint reduction on risk. Finally, this report shows that after accounting for the many vehicle, driver, and crash variables NHTSA used in its regression analyses, there remains a wide variation in risk by vehicle make and model, and this variation is unrelated to vehicle mass. Although the purpose of the NHTSA and LBNL reports is to estimate the effect of vehicle mass reduction on societal risk, this is not exactly what the regression models are estimating. Rather, they are estimating the recent historical relationship between mass and risk, after accounting for most measurable differences between vehicles, drivers, and crash times and locations. In essence, the regression models are comparing the risk of a 2600-lb Dodge Neon with that of a 2500-lb Honda Civic, after attempting to account for all other differences between the two vehicles. The models are not estimating the effect of literally removing 100 pounds from the Neon, leaving everything else unchanged. In addition, the analyses are based on the relationship of vehicle mass and footprint on risk for recent vehicle designs (model year 2004 to 2011). These relationships may or may not continue into the future as manufacturers utilize new vehicle designs and incorporate new technologies, such as more extensive use of strong lightweight materials and specific safety technologies. Therefore, throughout this report we use the phrase “the estimated effect of mass (or footprint) reduction on risk” as shorthand for “the estimated change in risk as a function of its relationship to mass (or footprint) for vehicle models of recent design.”« less

Two approaches to incorporate clinical data uncertainty into multiple criteria decision analysis for benefit-risk assessment of medicinal products.

PubMed

Wen, Shihua; Zhang, Lanju; Yang, Bo

2014-07-01

The Problem formulation, Objectives, Alternatives, Consequences, Trade-offs, Uncertainties, Risk attitude, and Linked decisions (PrOACT-URL) framework and multiple criteria decision analysis (MCDA) have been recommended by the European Medicines Agency for structured benefit-risk assessment of medicinal products undergoing regulatory review. The objective of this article was to provide solutions to incorporate the uncertainty from clinical data into the MCDA model when evaluating the overall benefit-risk profiles among different treatment options. Two statistical approaches, the δ-method approach and the Monte-Carlo approach, were proposed to construct the confidence interval of the overall benefit-risk score from the MCDA model as well as other probabilistic measures for comparing the benefit-risk profiles between treatment options. Both approaches can incorporate the correlation structure between clinical parameters (criteria) in the MCDA model and are straightforward to implement. The two proposed approaches were applied to a case study to evaluate the benefit-risk profile of an add-on therapy for rheumatoid arthritis (drug X) relative to placebo. It demonstrated a straightforward way to quantify the impact of the uncertainty from clinical data to the benefit-risk assessment and enabled statistical inference on evaluating the overall benefit-risk profiles among different treatment options. The δ-method approach provides a closed form to quantify the variability of the overall benefit-risk score in the MCDA model, whereas the Monte-Carlo approach is more computationally intensive but can yield its true sampling distribution for statistical inference. The obtained confidence intervals and other probabilistic measures from the two approaches enhance the benefit-risk decision making of medicinal products. Copyright © 2014 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Spatial modeling of cutaneous leishmaniasis in the Andean region of Colombia

PubMed Central

Pérez-Flórez, Mauricio; Ocampo, Clara Beatriz; Valderrama-Ardila, Carlos; Alexander, Neal

2016-01-01

The objective of this research was to identify environmental risk factors for cutaneous leishmaniasis (CL) in Colombia and map high-risk municipalities. The study area was the Colombian Andean region, comprising 715 rural and urban municipalities. We used 10 years of CL surveillance: 2000-2009. We used spatial-temporal analysis - conditional autoregressive Poisson random effects modelling - in a Bayesian framework to model the dependence of municipality-level incidence on land use, climate, elevation and population density. Bivariable spatial analysis identified rainforests, forests and secondary vegetation, temperature, and annual precipitation as positively associated with CL incidence. By contrast, livestock agroecosystems and temperature seasonality were negatively associated. Multivariable analysis identified land use - rainforests and agro-livestock - and climate - temperature, rainfall and temperature seasonality - as best predictors of CL. We conclude that climate and land use can be used to identify areas at high risk of CL and that this approach is potentially applicable elsewhere in Latin America. PMID:27355214

Use of multicriteria decision analysis for assessing the benefit and risk of over-the-counter analgesics.

PubMed

Moore, Andrew; Crossley, Anne; Ng, Bernard; Phillips, Lawrence; Sancak, Özgür; Rainsford, K D

2017-10-01

To test the ability of a multicriteria decision analysis (MCDA) model to incorporate disparate data sources of varying quality along with clinical judgement in a benefit-risk assessment of six well-known pain-relief drugs. Six over-the-counter (OTC) analgesics were evaluated against three favourable effects and eight unfavourable effects by seven experts who specialise in the relief of pain, two in a 2-day facilitated workshop whose input data and judgements were later peer-reviewed by five additional experts. Ibuprofen salts and solubilised emerged with the best benefit-risk profile, followed by naproxen, ibuprofen acid, diclofenac, paracetamol and aspirin. Multicriteria decision analysis enabled participants to evaluate the OTC analgesics against a range of favourable and unfavourable effects in a group setting that enabled all issues to be openly aired and debated. The model was easily communicated and understood by the peer reviewers, so the model should be comprehensible to physicians, pharmacists and other health professionals. © 2017 Royal Pharmaceutical Society.

Design Analysis Kit for Optimization and Terascale Applications 6.0

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

2015-10-19

Sandia's Dakota software (available at http://dakota.sandia.gov) supports science and engineering transformation through advanced exploration of simulations. Specifically it manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to: (1) enhance understanding of risk, (2) improve products, and (3) assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a computational model. However, Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, risk analysis, and quantification of margins and uncertainty with such models. It directly supports verificationmore » and validation activities. The algorithms implemented in Dakota aim to address challenges in performing these analyses with complex science and engineering models from desktop to high performance computers.« less

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.

Space Radiation Cancer Risk Projections and Uncertainties - 2010

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

NASA Astrophysics Data System (ADS)

Yu, Lean; Wang, Shouyang; Lai, K. K.

Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

[The methods of assessment of health risk from exposure to radon and radon daughters].

PubMed

Demin, V F; Zhukovskiy, M V; Kiselev, S M

2014-01-01

The critical analysis of existing models of the relationship dose-effect (RDE) for radon exposure on human health has been performed. Conclusion about the necessity and possibility of improving these models has been made. A new improved version ofthe RDE has been developed. A technique for assessing the human health risk of exposure to radon, including the method for estimating of exposure doses of radon, an improved model of RDE, proper methodology risk assessment has been described. Methodology is proposed for the use in the territory of Russia.

Scenario analysis and disaster preparedness for port and maritime logistics risk management.

PubMed

Kwesi-Buor, John; Menachof, David A; Talas, Risto

2016-08-01

System Dynamics (SD) modelling is used to investigate the impacts of policy interventions on industry actors' preparedness to mitigate risks and to recover from disruptions along the maritime logistics and supply chain network. The model suggests a bi-directional relation between regulation and industry actors' behaviour towards Disaster Preparedness (DP) in maritime logistics networks. The model also showed that the level of DP is highly contingent on forecast accuracy, technology change, attitude to risk prevention, port activities, and port environment. Copyright © 2016 Elsevier Ltd. All rights reserved.

HEALTH AND ENVIRONMENTAL SCIENCES INSTITUTE'S EXPOSURE FACTORS DATABASE FOR AGGREGATE AND CUMULATIVE RISK ASSESSMENT

EPA Science Inventory

In recent years, the risk analysis community has broadened its use of complex aggregate and cumulative residential exposure models (e.g., to meet the requirements of the 1996 Food Quality Protection Act). The value of these models is their ability to incorporate a range of input...

RNAV (GPS) total system error models for use in wake encounter risk analysis of candidate CSPR pairs for inclusion in FAA Order 7110.308

DOT National Transportation Integrated Search

2013-08-01

The purpose of this memorandum is to provide recommended Total System Error (TSE) models for : aircraft using RNAV (GPS) guidance when analyzing the wake encounter risk of proposed simultaneous : dependent (paired) approaches, with 1.5 Nautical...

RNAV (GPS) total system error models for use in wake encounter risk analysis of dependent paired approaches to closely-spaced parallel runways : Project memorandum - February 2014

DOT National Transportation Integrated Search

2014-02-01

The purpose of this memorandum is to provide recommended Total System Error (TSE) models : for aircraft using RNAV (GPS) guidance when analyzing the wake encounter risk of proposed : simultaneous dependent (paired) approach operations to Closel...

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.

Adaptive Governance, Uncertainty, and Risk: Policy Framing and Responses to Climate Change, Drought, and Flood.

PubMed

Hurlbert, Margot; Gupta, Joyeeta

2016-02-01

As climate change impacts result in more extreme events (such as droughts and floods), the need to understand which policies facilitate effective climate change adaptation becomes crucial. Hence, this article answers the question: How do governments and policymakers frame policy in relation to climate change, droughts, and floods and what governance structures facilitate adaptation? This research interrogates and analyzes through content analysis, supplemented by semi-structured qualitative interviews, the policy response to climate change, drought, and flood in relation to agricultural producers in four case studies in river basins in Chile, Argentina, and Canada. First, an epistemological explanation of risk and uncertainty underscores a brief literature review of adaptive governance, followed by policy framing in relation to risk and uncertainty, and an analytical model is developed. Pertinent findings of the four cases are recounted, followed by a comparative analysis. In conclusion, recommendations are made to improve policies and expand adaptive governance to better account for uncertainty and risk. This article is innovative in that it proposes an expanded model of adaptive governance in relation to "risk" that can help bridge the barrier of uncertainty in science and policy. © 2015 Society for Risk Analysis.

Vascular endothelial growth factor (VEGF-634G/C) polymorphism and retinopathy of prematurity: a meta-analysis

PubMed Central

Malik, Manzoor Ahmad; Shukla, Swati; Azad, Shorya Vardhan; Kaur, Jasbir

2014-01-01

Purpose Vascular endothelial growth factor polymorphism (VEGF-634G/C, rs 2010963) has been considered a risk factor for the development of retinopathy of prematurity (ROP). However, the results remain controversial. Therefore, the aim of the present meta-analysis was to determine the association between VEGF-634G/C polymorphism and ROP risk. Methods Published literature from PubMed and other databases were retrieved. All studies evaluating the association between VEGF-634G/C polymorphism and ROP risk were included. Pooled odds ratio (OR) and 95% confidence interval (CI) were calculated using random or fixed effects model. A total of six case-control studies including 355 cases and 471 controls were included. Results By pooling all the studies, we found that VEGF-634G/C polymorphism was not associated with ROP risk at co-dominant and allele levels and no association was also found in dominant and recessive models. While stratifying on ethnicity level no association was observed in Caucasian and Asian population. Discussion This meta-analysis suggests that VEGF-634G/C polymorphism may not be associated with ROP risk, the association between single VEGF-634G/C polymorphism and ROP risk awaits further investigation. PMID:25473347

Sensitivity Analysis of Median Lifetime on Radiation Risks Estimates for Cancer and Circulatory Disease amongst Never-Smokers

NASA Technical Reports Server (NTRS)

Chappell, Lori J.; Cucinotta, Francis A.

2011-01-01

Radiation risks are estimated in a competing risk formalism where age or time after exposure estimates of increased risks for cancer and circulatory diseases are folded with a probability to survive to a given age. The survival function, also called the life-table, changes with calendar year, gender, smoking status and other demographic variables. An outstanding problem in risk estimation is the method of risk transfer between exposed populations and a second population where risks are to be estimated. Approaches used to transfer risks are based on: 1) Multiplicative risk transfer models -proportional to background disease rates. 2) Additive risk transfer model -risks independent of background rates. In addition, a Mixture model is often considered where the multiplicative and additive transfer assumptions are given weighted contributions. We studied the influence of the survival probability on the risk of exposure induced cancer and circulatory disease morbidity and mortality in the Multiplicative transfer model and the Mixture model. Risks for never-smokers (NS) compared to the average U.S. population are estimated to be reduced between 30% and 60% dependent on model assumptions. Lung cancer is the major contributor to the reduction for NS, with additional contributions from circulatory diseases and cancers of the stomach, liver, bladder, oral cavity, esophagus, colon, a portion of the solid cancer remainder, and leukemia. Greater improvements in risk estimates for NS s are possible, and would be dependent on improved understanding of risk transfer models, and elucidating the role of space radiation on the various stages of disease formation (e.g. initiation, promotion, and progression).

Using probabilistic terrorism risk modeling for regulatory benefit-cost analysis: application to the Western hemisphere travel initiative in the land environment.

PubMed

Willis, Henry H; LaTourrette, Tom

2008-04-01

This article presents a framework for using probabilistic terrorism risk modeling in regulatory analysis. We demonstrate the framework with an example application involving a regulation under consideration, the Western Hemisphere Travel Initiative for the Land Environment, (WHTI-L). First, we estimate annualized loss from terrorist attacks with the Risk Management Solutions (RMS) Probabilistic Terrorism Model. We then estimate the critical risk reduction, which is the risk-reducing effectiveness of WHTI-L needed for its benefit, in terms of reduced terrorism loss in the United States, to exceed its cost. Our analysis indicates that the critical risk reduction depends strongly not only on uncertainties in the terrorism risk level, but also on uncertainty in the cost of regulation and how casualties are monetized. For a terrorism risk level based on the RMS standard risk estimate, the baseline regulatory cost estimate for WHTI-L, and a range of casualty cost estimates based on the willingness-to-pay approach, our estimate for the expected annualized loss from terrorism ranges from $2.7 billion to $5.2 billion. For this range in annualized loss, the critical risk reduction for WHTI-L ranges from 7% to 13%. Basing results on a lower risk level that results in halving the annualized terrorism loss would double the critical risk reduction (14-26%), and basing the results on a higher risk level that results in a doubling of the annualized terrorism loss would cut the critical risk reduction in half (3.5-6.6%). Ideally, decisions about terrorism security regulations and policies would be informed by true benefit-cost analyses in which the estimated benefits are compared to costs. Such analyses for terrorism security efforts face substantial impediments stemming from the great uncertainty in the terrorist threat and the very low recurrence interval for large attacks. Several approaches can be used to estimate how a terrorism security program or regulation reduces the distribution of risks it is intended to manage. But, continued research to develop additional tools and data is necessary to support application of these approaches. These include refinement of models and simulations, engagement of subject matter experts, implementation of program evaluation, and estimating the costs of casualties from terrorism events.

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

Treesearch

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

2013-01-01

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

Association between glutathione S-transferase P1 Ile (105) Val gene polymorphism and chronic obstructive pulmonary disease: A meta-analysis based on seventeen case-control studies.

PubMed

Yang, Lingjing; Li, Xixia; Tong, Xiang; Fan, Hong

2015-12-01

Previous studies have shown that glutathione S-transferase P1 (GSTP1) was associated with chronic obstructive pulmonary disease (COPD). However, the association between GSTP1 Ile (105) Val gene polymorphism and COPD remains controversial. To drive a more precise estimation, we performed a meta-analysis based on published case-control studies. An electronic search of PubMed, EMBASE, Cochrane library, Web of Science and China Knowledge Resource Integrated (CNKI) Database for papers on GSTP1 Ile (105) Val gene polymorphism and COPD risk was performed. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of association in the homozygote model, heterozygote model, dominant model, recessive model and an additive mode. Statistical heterogeneity, test of publication bias and sensitivity analysis was performed. The software STATA (Version 13.0) was used data analysis. Overall, seventeen studies with 1892 cases and 2012 controls were included in this meta-analysis. The GSTP1 Ile (105) Val polymorphism showed pooled odds ratios for the homozygote comparison (OR = 1.501, 95%CI [0.862, 2.614]), heterozygote comparison (OR = 0.924, 95%CI [0.733, 1.165]), dominant model (OR = 1.003, 95%CI [0.756, 1.331]), recessive model (OR = 1.510, 95%CI [0.934, 2.439]), and an additive model (OR = 1.072, 95%CI [0.822, 1.398]). In conclusion, the current meta-analysis, based on the most updated information, showed no significant association between GSTP1 Ile (105) Val gene polymorphism and COPD risk in any genetic models. The results of subgroup analysis also showed no significant association between GSTP1 Ile (105) Val gene polymorphism and COPD risk in Asian population and Caucasian population. Further studies involving large populations and careful control with age, sex, ethnicity, and cigarette smoking are greatly needed.

Association between glutathione S-transferase P1 Ile (105) Val gene polymorphism and chronic obstructive pulmonary disease: A meta-analysis based on seventeen case–control studies

PubMed Central

Yang, Lingjing; Li, Xixia; Tong, Xiang; Fan, Hong

2015-01-01

Introduction Previous studies have shown that glutathione S-transferase P1 (GSTP1) was associated with chronic obstructive pulmonary disease (COPD). However, the association between GSTP1 Ile (105) Val gene polymorphism and COPD remains controversial. To drive a more precise estimation, we performed a meta-analysis based on published case–control studies. Methods An electronic search of PubMed, EMBASE, Cochrane library, Web of Science and China Knowledge Resource Integrated (CNKI) Database for papers on GSTP1 Ile (105) Val gene polymorphism and COPD risk was performed. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of association in the homozygote model, heterozygote model, dominant model, recessive model and an additive mode. Statistical heterogeneity, test of publication bias and sensitivity analysis was performed. The software STATA (Version 13.0) was used data analysis. Results Overall, seventeen studies with 1892 cases and 2012 controls were included in this meta-analysis. The GSTP1 Ile (105) Val polymorphism showed pooled odds ratios for the homozygote comparison (OR = 1.501, 95%CI [0.862, 2.614]), heterozygote comparison (OR = 0.924, 95%CI [0.733, 1.165]), dominant model (OR = 1.003, 95%CI [0.756, 1.331]), recessive model (OR = 1.510, 95%CI [0.934, 2.439]), and an additive model (OR = 1.072, 95%CI [0.822, 1.398]). Conclusions In conclusion, the current meta-analysis, based on the most updated information, showed no significant association between GSTP1 Ile (105) Val gene polymorphism and COPD risk in any genetic models. The results of subgroup analysis also showed no significant association between GSTP1 Ile (105) Val gene polymorphism and COPD risk in Asian population and Caucasian population. Further studies involving large populations and careful control with age, sex, ethnicity, and cigarette smoking are greatly needed. PMID:26504746

Interaction of Reward Seeking and Self-Regulation in the Prediction of Risk Taking: A Cross-National Test of the Dual Systems Model

ERIC Educational Resources Information Center

Duell, Natasha; Steinberg, Laurence; Chein, Jason; Al-Hassan, Suha M.; Bacchini, Dario; Lei, Chang; Chaudhary, Nandita; Di Giunta, Laura; Dodge, Kenneth A.; Fanti, Kostas A.; Lansford, Jennifer E.; Malone, Patrick S.; Oburu, Paul; Pastorelli, Concetta; Skinner, Ann T.; Sorbring, Emma; Tapanya, Sombat; Uribe Tirado, Liliana Maria; Alampay, Liane Peña

2016-01-01

In the present analysis, we test the dual systems model of adolescent risk taking in a cross-national sample of over 5,200 individuals aged 10 through 30 (M = 17.05 years, SD = 5.91) from 11 countries. We examine whether reward seeking and self-regulation make independent, additive, or interactive contributions to risk taking, and ask whether…

Development of a Risk Assessment Tool to Predict Fall-Related Severe Injuries Occurring in a Hospital

PubMed Central

Toyabe, Shin-ichi

2014-01-01

Inpatient falls are the most common adverse events that occur in a hospital, and about 3 to 10% of falls result in serious injuries such as bone fractures and intracranial haemorrhages. We previously reported that bone fractures and intracranial haemorrhages were two major fall-related injuries and that risk assessment score for osteoporotic bone fracture was significantly associated not only with bone fractures after falls but also with intracranial haemorrhage after falls. Based on the results, we tried to establish a risk assessment tool for predicting fall-related severe injuries in a hospital. Possible risk factors related to fall-related serious injuries were extracted from data on inpatients that were admitted to a tertiary-care university hospital by using multivariate Cox’ s regression analysis and multiple logistic regression analysis. We found that fall risk score and fracture risk score were the two significant factors, and we constructed models to predict fall-related severe injuries incorporating these factors. When the prediction model was applied to another independent dataset, the constructed model could detect patients with fall-related severe injuries efficiently. The new assessment system could identify patients prone to severe injuries after falls in a reproducible fashion. PMID:25168984

Limits of Risk Predictability in a Cascading Alternating Renewal Process Model.

PubMed

Lin, Xin; Moussawi, Alaa; Korniss, Gyorgy; Bakdash, Jonathan Z; Szymanski, Boleslaw K

2017-07-27

Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model's prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.

The Analysis of Rush Orders Risk in Supply Chain: A Simulation Approach

NASA Technical Reports Server (NTRS)

Mahfouz, Amr; Arisha, Amr

2011-01-01

Satisfying customers by delivering demands at agreed time, with competitive prices, and in satisfactory quality level are crucial requirements for supply chain survival. Incidence of risks in supply chain often causes sudden disruptions in the processes and consequently leads to customers losing their trust in a company's competence. Rush orders are considered to be one of the main types of supply chain risks due to their negative impact on the overall performance, Using integrated definition modeling approaches (i.e. IDEF0 & IDEF3) and simulation modeling technique, a comprehensive integrated model has been developed to assess rush order risks and examine two risk mitigation strategies. Detailed functions sequence and objects flow were conceptually modeled to reflect on macro and micro levels of the studied supply chain. Discrete event simulation models were then developed to assess and investigate the mitigation strategies of rush order risks, the objective of this is to minimize order cycle time and cost.

A meta-analysis of MTHFR C677T and A1298C polymorphisms and risk of acute lymphoblastic leukemia in children.

PubMed

Yan, Jingrong; Yin, Ming; Dreyer, ZoAnn E; Scheurer, Michael E; Kamdar, Kala; Wei, Qingyi; Okcu, M Fatih

2012-04-01

Methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C polymorphisms have been implicated in childhood acute lymphoblastic leukemia (ALL) risk, but previously published studies were inconsistent and recent meta-analyses were not adequate. In a meta-analysis of 21 publications with 4,706 cases and 7,414 controls, we used more stringent inclusion method and summarized data on associations between MTHFR C677T and A1298C polymorphisms and childhood ALL risk. We found an overall association between 677T variant genotypes and reduced childhood ALL risk. Specifically, in the dominant genetic model, an association was found in a fixed-effect (TT + CT vs. CC: OR = 0.92; 95% CI = 0.85-0.99) but not random-effect model, whereas such an association was observed in both homozygote genetic model (TT vs. CC: OR = 0.80; 95% CI = 0.70-0.93 by fixed effects and OR = 0.78; 95% CI = 0.65-0.93 by random effects) and recessive genetic model (TT vs. CC + CT: OR = 0.83; 95% CI = 0.72-0.95 by fixed effects and OR = 0.84; 95% CI = 0.73-0.97 by random effects). These associations were also observed in subgroups by ethnicity: for Asians in all models except for the dominant genetic model by random effect and for Caucasians in all models except for the recessive genetic model. However, the A1298C polymorphism did not appear to have an effect on childhood ALL risk. These results suggest that the MTHFR C677T, but not A1298C, polymorphism is a potential biomarker for childhood ALL risk. Copyright © 2011 Wiley Periodicals, Inc.

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.

Population viability analysis for endangered Roanoke logperch

USGS Publications Warehouse

Roberts, James H.; Angermeier, Paul; Anderson, Gregory B.

2016-01-01

A common strategy for recovering endangered species is ensuring that populations exceed the minimum viable population size (MVP), a demographic benchmark that theoretically ensures low long-term extinction risk. One method of establishing MVP is population viability analysis, a modeling technique that simulates population trajectories and forecasts extinction risk based on a series of biological, environmental, and management assumptions. Such models also help identify key uncertainties that have a large influence on extinction risk. We used stochastic count-based simulation models to explore extinction risk, MVP, and the possible benefits of alternative management strategies in populations of Roanoke logperch Percina rex, an endangered stream fish. Estimates of extinction risk were sensitive to the assumed population growth rate and model type, carrying capacity, and catastrophe regime (frequency and severity of anthropogenic fish kills), whereas demographic augmentation did little to reduce extinction risk. Under density-dependent growth, the estimated MVP for Roanoke logperch ranged from 200 to 4200 individuals, depending on the assumed severity of catastrophes. Thus, depending on the MVP threshold, anywhere from two to all five of the logperch populations we assessed were projected to be viable. Despite this uncertainty, these results help identify populations with the greatest relative extinction risk, as well as management strategies that might reduce this risk the most, such as increasing carrying capacity and reducing fish kills. Better estimates of population growth parameters and catastrophe regimes would facilitate the refinement of MVP and extinction-risk estimates, and they should be a high priority for future research on Roanoke logperch and other imperiled stream-fish species.

Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

NASA Technical Reports Server (NTRS)

Dezfuli, Homayoon; Kelly, Dana; Smith, Curtis; Vedros, Kurt; Galyean, William

2009-01-01

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

Field modeling of heat transfer in atrium

NASA Astrophysics Data System (ADS)

Nedryshkin, Oleg; Gravit, Marina; Bushuev, Nikolay

2017-10-01

The results of calculating fire risk are an important element in the system of modern fire safety assessment. The article reviews the work on the mathematical modeling of fire in the room. A comparison of different calculation models in the programs of fire risk assessment and fire modeling was performed. The results of full-scale fire tests and fire modeling in the FDS program are presented. The analysis of empirical and theoretical data on fire modeling is made, a conclusion is made about the modeling accuracy in the FDS program.

External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery.

PubMed

Hickey, Graeme L; Blackstone, Eugene H

2016-08-01

Clinical risk-prediction models serve an important role in healthcare. They are used for clinical decision-making and measuring the performance of healthcare providers. To establish confidence in a model, external model validation is imperative. When designing such an external model validation study, thought must be given to patient selection, risk factor and outcome definitions, missing data, and the transparent reporting of the analysis. In addition, there are a number of statistical methods available for external model validation. Execution of a rigorous external validation study rests in proper study design, application of suitable statistical methods, and transparent reporting. Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Overview of the Special Issue: A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

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

Waldhoff, Stephanie T.; Martinich, Jeremy; Sarofim, Marcus

2015-07-01

The Climate Change Impacts and Risk Analysis (CIRA) modeling exercise is a unique contribution to the scientific literature on climate change impacts, economic damages, and risk analysis that brings together multiple, national-scale models of impacts and damages in an integrated and consistent fashion to estimate climate change impacts, damages, and the benefits of greenhouse gas (GHG) mitigation actions in the United States. The CIRA project uses three consistent socioeconomic, emissions, and climate scenarios across all models to estimate the benefits of GHG mitigation policies: a Business As Usual (BAU) and two policy scenarios with radiative forcing (RF) stabilization targets ofmore » 4.5 W/m2 and 3.7 W/m2 in 2100. CIRA was also designed to specifically examine the sensitivity of results to uncertainties around climate sensitivity and differences in model structure. The goals of CIRA project are to 1) build a multi-model framework to produce estimates of multiple risks and impacts in the U.S., 2) determine to what degree risks and damages across sectors may be lowered from a BAU to policy scenarios, 3) evaluate key sources of uncertainty along the causal chain, and 4) provide information for multiple audiences and clearly communicate the risks and damages of climate change and the potential benefits of mitigation. This paper describes the motivations, goals, and design of the CIRA modeling exercise and introduces the subsequent papers in this special issue.« less

Association between toll-like receptors 9 (TLR9) gene polymorphism and risk of pulmonary tuberculosis: meta-analysis.

PubMed

Chen, Zhi; Wang, Wei; Liang, Jianqin; Wang, Jinhe; Feng, Shisheng; Zhang, Guangyu

2015-05-08

Previous studies indicated that the single nucleotide polymorphisms (SNPs) in TLR9 gene might be associated with Tuberculosis (TB) risk. However, the results are inconsistent and inconclusive. 1745 articles from four databases were involved in our study. A meta-analysis on the associations between the seven polymorphisms and TB risk was carried out by comparison using different genetic models. In this systematic review 8 studies from seven English articles were analyzed. Our results showed that rs352139 is significantly associated with TB risk (AA vs. AG, OR 0.77, 95% CI 0.65-0.92, P = 0.004). In the ethnic subgroup analysis, Indonesians with AA genotype had a decreased susceptibility while Mexicans with GG allele had an increased risk. The meta-analysis indicated that rs352139 polymorphism might be associated with decreased TB risk in Indonesians whereas increased risk in Mexicans. Whether the observed association was due to causal effect needs to be further studied.

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

NASA Technical Reports Server (NTRS)

Luxhoj, J. T.; Ancel, E.; Green, L. L.; Shih, A. T.; Jones, S. M.; Reveley, M. S.

2014-01-01

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

New risk metrics and mathematical tools for risk analysis: Current and future challenges

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

Skandamis, Panagiotis N., E-mail: pskan@aua.gr; Andritsos, Nikolaos, E-mail: pskan@aua.gr; Psomas, Antonios, E-mail: pskan@aua.gr

The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) themore » Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total ‘failure’ that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user-friendly softwares, (e.g., Seafood Spoilage Predictor) have evolved the use of information systems in the food safety management. Such tools are updateable with new food-pathogen specific models containing cardinal parameters and multiple dependent variables, including plate counts, concentration of metabolic products, or even expression levels of certain genes. Then, these tools may further serve as decision-support tools which may assist in product logistics, based on their scientifically-based and “momentary” expressed spoilage and safety level.« less

New risk metrics and mathematical tools for risk analysis: Current and future challenges

NASA Astrophysics Data System (ADS)

Skandamis, Panagiotis N.; Andritsos, Nikolaos; Psomas, Antonios; Paramythiotis, Spyridon

2015-01-01

The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total `failure' that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user-friendly softwares, (e.g., Seafood Spoilage Predictor) have evolved the use of information systems in the food safety management. Such tools are updateable with new food-pathogen specific models containing cardinal parameters and multiple dependent variables, including plate counts, concentration of metabolic products, or even expression levels of certain genes. Then, these tools may further serve as decision-support tools which may assist in product logistics, based on their scientifically-based and "momentary" expressed spoilage and safety level.

Detection of major climatic and environmental predictors of liver fluke exposure risk in Ireland using spatial cluster analysis.

PubMed

Selemetas, Nikolaos; de Waal, Theo

2015-04-30

Fasciolosis caused by Fasciola hepatica (liver fluke) can cause significant economic and production losses in dairy cow farms. The aim of the current study was to identify important weather and environmental predictors of the exposure risk to liver fluke by detecting clusters of fasciolosis in Ireland. During autumn 2012, bulk-tank milk samples from 4365 dairy farms were collected throughout Ireland. Using an in-house antibody-detection ELISA, the analysis of BTM samples showed that 83% (n=3602) of dairy farms had been exposed to liver fluke. The Getis-Ord Gi* statistic identified 74 high-risk and 130 low-risk significant (P<0.01) clusters of fasciolosis. The low-risk clusters were mostly located in the southern regions of Ireland, whereas the high-risk clusters were mainly situated in the western part. Several climatic variables (monthly and seasonal mean rainfall and temperatures, total wet days and rain days) and environmental datasets (soil types, enhanced vegetation index and normalised difference vegetation index) were used to investigate dissimilarities in the exposure to liver fluke between clusters. Rainfall, total wet days and rain days, and soil type were the significant classes of climatic and environmental variables explaining the differences between significant clusters. A discriminant function analysis was used to predict the exposure risk to liver fluke using 80% of data for modelling and the remaining subset of 20% for post hoc model validation. The most significant predictors of the model risk function were total rainfall in August and September and total wet days. The risk model presented 100% sensitivity and 91% specificity and an accuracy of 95% correctly classified cases. A risk map of exposure to liver fluke was constructed with higher probability of exposure in western and north-western regions. The results of this study identified differences between clusters of fasciolosis in Ireland regarding climatic and environmental variables and detected significant predictors of the exposure risk to liver fluke. Copyright © 2015 Elsevier B.V. All rights reserved.

Applying Latent Class Analysis to Risk Stratification for Perioperative Mortality in Patients Undergoing Intraabdominal General Surgery.

PubMed

Kim, Minjae; Wall, Melanie M; Li, Guohua

2016-07-01

Perioperative risk stratification is often performed using individual risk factors without consideration of the syndemic of these risk factors. We used latent class analysis (LCA) to identify the classes of comorbidities and risk factors associated with perioperative mortality in patients presenting for intraabdominal general surgery. The 2005 to 2010 American College of Surgeons National Surgical Quality Improvement Program was used to obtain a cohort of patients undergoing intraabdominal general surgery. Risk factors and comorbidities were entered into LCA models to identify the latent classes, and individuals were assigned to a class based on the highest posterior probability of class membership. Relative risk regression was used to determine the associations between the latent classes and 30-day mortality, with adjustments for procedure. A 9-class model was fit using LCA on 466,177 observations. After combining classes with similar adjusted mortality risks, 5 risk classes were obtained. Compared with the class with average mortality risk (class 4), the risk ratios (95% confidence interval) ranged from 0.020 (0.014-0.027) in the lowest risk class (class 1) to 6.75 (6.46-7.02) in the highest risk class. After adjusting for procedure and ASA physical status, the latent classes remained significantly associated with 30-day mortality. The addition of the risk class variable to a model containing ASA physical status and surgical procedure demonstrated a significant increase in the area under the receiver operator characteristic curve (0.892 vs 0.915; P < 0.0001). Latent classes of risk factors and comorbidities in patients undergoing intraabdominal surgery are predictive of 30-day mortality independent of the ASA physical status and improve risk prediction with the ASA physical status.

Constellation Probabilistic Risk Assessment (PRA): Design Consideration for the Crew Exploration Vehicle

NASA Technical Reports Server (NTRS)

Prassinos, Peter G.; Stamatelatos, Michael G.; Young, Jonathan; Smith, Curtis

2010-01-01

Managed by NASA's Office of Safety and Mission Assurance, a pilot probabilistic risk analysis (PRA) of the NASA Crew Exploration Vehicle (CEV) was performed in early 2006. The PRA methods used follow the general guidance provided in the NASA PRA Procedures Guide for NASA Managers and Practitioners'. Phased-mission based event trees and fault trees are used to model a lunar sortie mission of the CEV - involving the following phases: launch of a cargo vessel and a crew vessel; rendezvous of these two vessels in low Earth orbit; transit to th$: moon; lunar surface activities; ascension &om the lunar surface; and return to Earth. The analysis is based upon assumptions, preliminary system diagrams, and failure data that may involve large uncertainties or may lack formal validation. Furthermore, some of the data used were based upon expert judgment or extrapolated from similar componentssystemsT. his paper includes a discussion of the system-level models and provides an overview of the analysis results used to identify insights into CEV risk drivers, and trade and sensitivity studies. Lastly, the PRA model was used to determine changes in risk as the system configurations or key parameters are modified.

A meta-analysis of interleukin-10-1082 promoter genetic polymorphism associated with atherosclerotic risk.

PubMed

Chao, Li; Lei, Huang; Fei, Jin

2014-01-01

This meta-analysis was conducted to assess the relationship between interleukin-10-1082 G/A single nucleotide polymorphism with atherosclerosis (AS) risk. The databases of PubMed, EMBASE, Chinese National Knowledge Infrastructure and Wan-Fang were searched from January 2000 to January 2014. 16 studies (involving 7779 cases and 7271 controls) were finally included. Each eligible study was scored for quality assessment. We adopted the most probably appropriate genetic model (recessive model) after carefully calculation. Between study heterogeneity was explored by subgroup analysis and publication bias was estimated by Begg's funnel plot and Egger's regression test. Statistically significant association was observed between AA genotype with overall AS risk, being mainly in coronary heart disease and stroke subgroups among Asian population, and peripheral artery disease (PAD) subgroup among Caucasians. Interleukin-10-1082 AA genotype is associated with increased overall AS risk. AA carriers of Asians seem to be more susceptible to coronary artery disease and stroke, and Caucasians are more susceptible to PAD.

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

PubMed

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

2013-08-01

Exotic animal diseases (EADs) are characterized by their capacity to spread global distances, causing impacts on animal health and welfare with significant economic consequences. We offer a critique of current import risk analysis approaches employed in the EAD field, focusing on their capacity to assess complex systems at a policy level. To address the shortcomings identified, we propose a novel method providing a systematic analysis of the likelihood of a disease incursion, developed by reference to the multibarrier system employed for the United Kingdom. We apply the network model to a policy-level risk assessment of classical swine fever (CSF), a notifiable animal disease caused by the CSF virus. In doing so, we document and discuss a sequence of analyses that describe system vulnerabilities and reveal the critical control points (CCPs) for intervention, reducing the likelihood of U.K. pig herds being exposed to the CSF virus. © 2012 Society for Risk Analysis.

Fasting insulin, insulin resistance and risk of hypertension in the general population: A meta-analysis.

PubMed

Wang, Feng; Han, Lili; Hu, Dayi

2017-01-01

Studies on the association of fasting insulin concentrations or insulin resistance with subsequent risk of hypertension have yielded conflicting results. To quantitatively assess the association of fasting insulin concentrations or homeostasis model assessment insulin resistance (HOMA-IR) with incident hypertension in a general population by performing a meta-analysis. We searched the PubMed and Embase databases until August 31, 2016 for prospective observational studies investigating the elevated fasting insulin concentrations or HOMA-IR with subsequent risk of hypertension in the general population. Pooled risk ratio (RR) and 95% confidence interval (CI) of hypertension was calculated for the highest versus the lowest category of fasting insulin or HOMA-IR. Eleven studies involving 10,230 hypertension cases were identified from 55,059 participants. Meta-analysis showed that the pooled adjusted RR of hypertension was 1.54 (95% CI 1.34-1.76) for fasting insulin concentrations and 1.43 (95% CI 1.27-1.62) for HOMA-IR comparing the highest to the lowest category. Subgroup analysis results showed that the association of fasting insulin concentrations with subsequent risk of hypertension seemed more pronounced in women (RR 2.07; 95% CI 1.19-3.60) than in men (RR 1.48; 95% CI 1.17-1.88). This meta-analysis suggests that elevated fasting insulin concentrations or insulin resistance as estimated by homeostasis model assessment is independently associated with an exacerbated risk of hypertension in the general population. Early intervention of hyperinsulinemia or insulin resistance may help clinicians to identify the high risk of hypertensive population. Copyright © 2016 Elsevier B.V. All rights reserved.

A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

PubMed

Meads, Catherine; Ahmed, Ikhlaaq; Riley, Richard D

2012-04-01

A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the 'Gail 2' model showed the average C statistic was 0.63 (95% CI 0.59-0.67), and the expected/observed ratio of events varied considerably across studies (95% prediction interval for E/O ratio when the model was applied in practice was 0.75-1.19). There is a need for models with better predictive performance but, given the large amount of work already conducted, further improvement of existing models based on conventional risk factors is perhaps unlikely. Research to identify new risk factors with large additionally predictive ability is therefore needed, alongside clearer reporting and continual validation of new models as they develop.

Predicting the Risk of Breakthrough Urinary Tract Infections: Primary Vesicoureteral Reflux.

PubMed

Hidas, Guy; Billimek, John; Nam, Alexander; Soltani, Tandis; Kelly, Maryellen S; Selby, Blake; Dorgalli, Crystal; Wehbi, Elias; McAleer, Irene; McLorie, Gordon; Greenfield, Sheldon; Kaplan, Sherrie H; Khoury, Antoine E

2015-11-01

We constructed a risk prediction instrument stratifying patients with primary vesicoureteral reflux into groups according to their 2-year probability of breakthrough urinary tract infection. Demographic and clinical information was retrospectively collected in children diagnosed with primary vesicoureteral reflux and followed for 2 years. Bivariate and binary logistic regression analyses were performed to identify factors associated with breakthrough urinary tract infection. The final regression model was used to compute an estimation of the 2-year probability of breakthrough urinary tract infection for each subject. Accuracy of the binary classifier for breakthrough urinary tract infection was evaluated using receiver operator curve analysis. Three distinct risk groups were identified. The model was then validated in a prospective cohort. A total of 252 bivariate analyses showed that high grade (IV or V) vesicoureteral reflux (OR 9.4, 95% CI 3.8-23.5, p <0.001), presentation after urinary tract infection (OR 5.3, 95% CI 1.1-24.7, p = 0.034) and female gender (OR 2.6, 95% CI 0.097-7.11, p <0.054) were important risk factors for breakthrough urinary tract infection. Subgroup analysis revealed bladder and bowel dysfunction was a significant risk factor more pronounced in low grade (I to III) vesicoureteral reflux (OR 2.8, p = 0.018). The estimation model was applied for prospective validation, which demonstrated predicted vs actual 2-year breakthrough urinary tract infection rates of 19% vs 21%. Stratifying the patients into 3 risk groups based on parameters in the risk model showed 2-year risk for breakthrough urinary tract infection was 8.6%, 26.0% and 62.5% in the low, intermediate and high risk groups, respectively. This proposed risk stratification and probability model allows prediction of 2-year risk of patient breakthrough urinary tract infection to better inform parents of possible outcomes and treatment strategies. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Genetic Polymorphism of Angiotensin-Converting Enzyme and Chronic Obstructive Pulmonary Disease Risk: An Updated Meta-Analysis

PubMed Central

Kang, Sang Wook; Kim, Su Kang; Jung, Hee-Jae; Kim, Kwan-Il; Kim, Jinju

2016-01-01

The relationship between polymorphism of the angiotensin I converting enzyme (ACE) gene and chronic obstructive pulmonary disease (COPD) has been examined in many previous studies. However, their results were controversial. Therefore, we performed a meta-analysis to evaluate the relationship between the ACE gene and the risk of COPD. Fourteen case-control studies were included in this meta-analysis. The pooled p value, odds ratio (OR), and 95% confidence interval (95% CI) were used to investigate the strength of the association. The meta-analysis was performed using comprehensive meta-analysis software. Our meta-analysis results revealed that ACE polymorphisms were not related to the risk of COPD (p > 0.05 in each model). In further analyses based on ethnicity, we observed an association between insertion/deletion polymorphism of the ACE gene and risk of COPD in the Asian population (codominant 2, OR = 3.126, 95% CI = 1.919–5.093, p < 0.001; recessive, OR = 3.326, 95% CI = 2.190–5.050, p < 0.001) but not in the Caucasian population (p > 0.05 in each model). In conclusion, the present meta-analysis indicated that the insertion/deletion polymorphism of the ACE gene may be associated with susceptibility to COPD in the Asian population but not in the Caucasian population. However, the results of the present meta-analysis need to be confirmed in a larger sample. PMID:27830153

Building electronic forms for elderly program: integrated care model for high risk elders in Hong Kong.

PubMed

Yiu, Rex; Fung, Vicky; Szeto, Karen; Hung, Veronica; Siu, Ricky; Lam, Johnny; Lai, Daniel; Maw, Christina; Cheung, Adah; Shea, Raman; Choy, Anna

2013-01-01

In Hong Kong, elderly patients discharged from hospital are at high risk of unplanned readmission. The Integrated Care Model (ICM) program is introduced to provide continuous and coordinated care for high risk elders from hospital to community to prevent unplanned readmission. A multidisciplinary working group was set up to address the requirements on developing the electronic forms for ICM program. Six (6) forms were developed. These forms can support ICM service delivery for the high risk elders, clinical documentation, statistical analysis and information sharing.

Flood risk in a changing world - a coupled transdisciplinary modelling framework for flood risk assessment in an Alpine study area

NASA Astrophysics Data System (ADS)

Huttenlau, Matthias; Schneeberger, Klaus; Winter, Benjamin; Pazur, Robert; Förster, Kristian; Achleitner, Stefan; Bolliger, Janine

2017-04-01

Devastating flood events have caused substantial economic damage across Europe during past decades. Flood risk management has therefore become a topic of crucial interest across state agencies, research communities and the public sector including insurances. There is consensus that mitigating flood risk relies on impact assessments which quantitatively account for a broad range of aspects in a (changing) environment. Flood risk assessments which take into account the interaction between the drivers climate change, land-use change and socio-economic change might bring new insights to the understanding of the magnitude and spatial characteristic of flood risks. Furthermore, the comparative assessment of different adaptation measures can give valuable information for decision-making. With this contribution we present an inter- and transdisciplinary research project aiming at developing and applying such an impact assessment relying on a coupled modelling framework for the Province of Vorarlberg in Austria. Stakeholder engagement ensures that the final outcomes of our study are accepted and successfully implemented in flood management practice. The study addresses three key questions: (i) What are scenarios of land- use and climate change for the study area? (ii) How will the magnitude and spatial characteristic of future flood risk change as a result of changes in climate and land use? (iii) Are there spatial planning and building-protection measures which effectively reduce future flood risk? The modelling framework has a modular structure comprising modules (i) climate change, (ii) land-use change, (iii) hydrologic modelling, (iv) flood risk analysis, and (v) adaptation measures. Meteorological time series are coupled with spatially explicit scenarios of land-use change to model runoff time series. The runoff time series are combined with impact indicators such as building damages and results are statistically assessed to analyse flood risk scenarios. Thus, the regional flood risk can be expressed in terms of expected annual damage and damages associated with a low probability of occurrence. We consider building protection measures explicitly as part of the consequence analysis of flood risk whereas spatial planning measures are already considered as explicit scenarios in the course of land-use change modelling.

A flexible count data regression model for risk analysis.

PubMed

Guikema, Seth D; Coffelt, Jeremy P; Goffelt, Jeremy P

2008-02-01

In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.

A Model for Risk Analysis of Oil Tankers

NASA Astrophysics Data System (ADS)

Montewka, Jakub; Krata, Przemysław; Goerland, Floris; Kujala, Pentti

2010-01-01

The paper presents a model for risk analysis regarding marine traffic, with the emphasis on two types of the most common marine accidents which are: collision and grounding. The focus is on oil tankers as these pose the highest environmental risk. A case study in selected areas of Gulf of Finland in ice free conditions is presented. The model utilizes a well-founded formula for risk calculation, which combines the probability of an unwanted event with its consequences. Thus the model is regarded a block type model, consisting of blocks for the probability of collision and grounding estimation respectively as well as blocks for consequences of an accident modelling. Probability of vessel colliding is assessed by means of a Minimum Distance To Collision (MDTC) based model. The model defines in a novel way the collision zone, using mathematical ship motion model and recognizes traffic flow as a non homogeneous process. The presented calculations address waterways crossing between Helsinki and Tallinn, where dense cross traffic during certain hours is observed. For assessment of a grounding probability, a new approach is proposed, which utilizes a newly developed model, where spatial interactions between objects in different locations are recognized. A ship at a seaway and navigational obstructions may be perceived as interacting objects and their repulsion may be modelled by a sort of deterministic formulation. Risk due to tankers running aground addresses an approach fairway to an oil terminal in Sköldvik, near Helsinki. The consequences of an accident are expressed in monetary terms, and concern costs of an oil spill, based on statistics of compensations claimed from the International Oil Pollution Compensation Funds (IOPC Funds) by parties involved.

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.

Cognitive task analysis: harmonizing tasks to human capacities.

PubMed

Neerincx, M A; Griffioen, E

1996-04-01

This paper presents the development of a cognitive task analysis that assesses the task load of jobs and provides indicators for the redesign of jobs. General principles of human task performance were selected and, subsequently, integrated into current task modelling techniques. The resulting cognitive task analysis centres around four aspects of task load: the number of actions in a period, the ratio between knowledge- and rule-based actions, lengthy uninterrupted actions, and momentary overloading. The method consists of three stages: (1) construction of a hierarchical task model, (2) a time-line analysis and task load assessment, and (3), if necessary, adjustment of the task model. An application of the cognitive task analysis in railway traffic control showed its benefits over the 'old' task load analysis of the Netherlands Railways. It provided a provisional standard for traffic control jobs, conveyed two load risks -- momentary overloading and underloading -- and resulted in proposals to satisfy the standard and to diminish the two load risk.

Association of N-acetyltransferase 1 polymorphism and bladder cancer risk: an updated meta-analysis and trial sequential analysis.

PubMed

Xu, Zicheng; Li, Xiao; Qin, Zhiqiang; Xue, Jianxin; Wang, Jingyuan; Liu, Zhentao; Cai, Hongzhou; Yu, Bin; Xu, Ting; Zou, Qin

2017-07-24

Individual studies of the association between N-acetyltransferase 1 (NAT1)*10 allele and bladder cancer susceptibility have shown inconclusive results. To derive a more precise estimation of any such relationship, we performed this systemic review and updated meta-analysis based on 17 publications. A total of 17 studies were investigated with 4,322 bladder cancer cases and 4,944 controls. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of the association. Subgroup analyses were conducted based on ethnicity, sex, source of controls and detecting methods. Then trial sequential analysis was performed to evaluate whether the evidence of the results was sufficient and reduce the risk of type I error. There was no association between NAT1*10 allele and bladder cancer risk in a random-effects model (OR = 0.96, 95% CI, 0.84-1.10) or in a fixed-effects model (OR = 0.95, 95% CI, 0.87-1.03). In addition, no significantly increased risk of bladder cancer was found in any other subgroup analysis. Then, trial sequential analyses demonstrated that the results of our study need to be further verified. Despite its limitations, the results of the present meta-analysis suggested that there was no association between NAT1*10 allele and bladder cancer risk. More importantly, our findings need to be further validated regarding whether being without the NAT1*10 allele could in the future be shown to be a potential marker for the risk of bladder cancer.

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.

Translating reference doses into allergen management practice: challenges for stakeholders.

PubMed

Crevel, René W R; Baumert, Joseph L; Luccioli, Stefano; Baka, Athanasia; Hattersley, Sue; Hourihane, Jonathan O'B; Ronsmans, Stefan; Timmermans, Frans; Ward, Rachel; Chung, Yong-joo

2014-05-01

Risk assessment describes the impact of a particular hazard as a function of dose and exposure. It forms the foundation of risk management and contributes to the overall decision-making process, but is not its endpoint. This paper outlines a risk analysis framework to underpin decision-making in the area of allergen cross-contact. Specifically, it identifies challenges relevant to each component of the risk analysis: risk assessment (data gaps and output interpretation); risk management (clear and realistic objectives); and risk communication (clear articulation of risk and benefit). Translation of the outputs from risk assessment models into risk management measures must be informed by a clear understanding of the model outputs and their limitations. This will lead to feasible and achievable risk management objectives, grounded in a level of risk accepted by the different stakeholders, thereby avoiding potential unintended detrimental consequences. Clear, consistent and trustworthy communications actively involving all stakeholders underpin these objectives. The conclusions, integrating the perspectives of different stakeholders, offer a vision where clear, science-based benchmarks form the basis of allergen management and labelling, cutting through the current confusion and uncertainty. Finally, the paper recognises that the proposed framework must be adaptable to new and emerging evidence. Copyright © 2014 ILSI Europe. Published by Elsevier Ltd.. All rights reserved.

Segregation analysis of prostate cancer in France: evidence for autosomal dominant inheritance and residual brother-brother dependence.

PubMed

Valeri, A; Briollais, L; Azzouzi, R; Fournier, G; Mangin, P; Berthon, P; Cussenot, O; Demenais, F

2003-03-01

Four segregation analyses concerning prostate cancer (CaP), three conducted in the United States and one in Northern Europe, have shown evidence for a dominant major gene but with different parameter estimates. A recent segregation analysis of Australian pedigrees has found a better fit of a two-locus model than single-locus models. This model included a dominantly inherited increased risk that was greater at younger ages and a recessively inherited or X-linked increased risk that was greater at older ages. Recent linkage analyses have led to the detection of at least 8 CaP predisposing genes, suggesting a complex inheritance and genetic heterogeneity. To assess the nature of familial aggregation of prostate cancer in France, segregation analysis was conducted in 691 families ascertained through 691 CaP patients, recruited from three French hospitals and unselected with respect to age at diagnosis, clinical stage or family history. This mode of family inclusion, without any particular selection of the probands, is unique, as probands from all previous analyses were selected according to various criteria. Segregation analysis was carried out using the logistic hazard regressive model, as incorporated in the REGRESS program, which can accommodate a major gene effect, residual familial dependences of any origin (genetic and/or environmental), and covariates, while including survival analysis concepts. Segregation analysis showed evidence for the segregation of an autosomal dominant gene (allele frequency of 0.03%) with an additional brother-brother dependence. The estimated cumulative risks of prostate cancer by age 85 years, among subjects with the at-risk genotype, were 86% in the fathers' generation and 99% in the probands' generation. This study supports the model of Mendelian transmission of a rare autosomal dominant gene with high penetrance, and demonstrates that additional genetic and/or common sibling environmental factors are involved to account for the familial clustering of CaP.

Risk score elaboration for mediastinitis after coronary artery bypass grafting.

PubMed

Magedanz, Ellen Hettwer; Bodanese, Luiz Carlos; Guaragna, João Carlos Vieira da Costa; Albuquerque, Luciano Cabral; Martins, Valério; Minossi, Silvia Daniela; Piccoli, Jacqueline da Costa Escobar; Goldani, Marco Antônio

2010-01-01

The mediastinitis is a serious postoperative complication of cardiac surgery, with an incidence of 0.4 to 5% and mortality between 14 and 47%. Several models were proposed to assess risk of mediastinitis after cardiac surgery. However, most of these models do not evaluate the postoperative morbidity. This study aims to develop a score risk model to predict the risk of mediastinitis for patients undergoing coronary artery bypass grafting. The study sample included data from 2,809 adult patients undergoing coronary artery bypass grafting between January 1996 and December 2007 at Hospital São Lucas -PUCRS. Logistic regression was used to examine the relationship between risk factors and the development of mediastinitis. Data from 1,889 patients were used to develop the model and its performance was evaluated in the remaining data (n=920). The definitive model was created with the data analysis of 2,809 patients. The rate of mediastinitis was 3.3%, with mortality of 26.6%. In the multivariate analysis, five variables remained independent predictors of the outcome: chronic obstructive pulmonary disease, obesity, surgical reintervention, blood transfusion and stable angina class IV or unstable. The area under the ROC curve was 0.72 (95% CI, 0.67-0.78) and P = 0.61. The risk score was constructed for use in daily practice to calculate the rate of mediastinitis after coronary artery bypass grafting. The score includes routinely collected variables and is simple to use.

Analysis of Alternatives for Risk Assessment Methodologies and Tools

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

Nachtigal, Noel M.; Fruetel, Julia A.; Gleason, Nathaniel J.

The purpose of this document is to provide a basic overview and understanding of risk assessment methodologies and tools from the literature and to assess the suitability of these methodologies and tools for cyber risk assessment. Sandia National Laboratories (SNL) performed this review in support of risk modeling activities performed for the Stakeholder Engagement and Cyber Infrastructure Resilience (SECIR) division of the Department of Homeland Security (DHS) Office of Cybersecurity and Communications (CS&C). The set of methodologies and tools covered in this document is not intended to be exhaustive; instead, it focuses on those that are commonly used in themore » risk assessment community. The classification of methodologies and tools was performed by a group of analysts with experience in risk analysis and cybersecurity, and the resulting analysis of alternatives has been tailored to address the needs of a cyber risk assessment.« less

AQUATOX coupled foodweb model for ecosystem risk assessment of Polybrominated diphenyl ethers (PBDEs) in lake ecosystems.

PubMed

Zhang, Lulu; Liu, Jingling

2014-08-01

The AQUATOX model considers the direct toxic effects of chemicals and their indirect effects through foodwebs. For this study, the AQUATOX model was applied to evaluating the ecological risk of Polybrominated diphenyl ethers (PBDEs) in a highly anthropogenically disturbed lake-Baiyangdian Lake. Calibration and validation results indicated that the model can adequately describe the dynamics of 18 biological populations. Sensitivity analysis results suggested that the model is highly sensitive to temperature limitation. PBDEs risk estimate results demonstrate that estimated risk for natural ecosystems cannot be fully explained by single species toxicity data alone. The AQUATOX model could provide a good basis in ascertaining ecological protection levels of "chemicals of concern" for aquatic ecosystems. Therefore, AQUATOX can potentially be used to provide necessary information corresponding to early warning and rapid forecasting of pollutant transport and fate in the management of chemicals that put aquatic ecosystems at risk. Copyright © 2014 Elsevier Ltd. All rights reserved.

Launch Vehicle Debris Models and Crew Vehicle Ascent Abort Risk

NASA Technical Reports Server (NTRS)

Gee, Ken; Lawrence, Scott

2013-01-01

For manned space launch systems, a reliable abort system is required to reduce the risks associated with a launch vehicle failure during ascent. Understanding the risks associated with failure environments can be achieved through the use of physics-based models of these environments. Debris fields due to destruction of the launch vehicle is one such environment. To better analyze the risk posed by debris, a physics-based model for generating launch vehicle debris catalogs has been developed. The model predicts the mass distribution of the debris field based on formulae developed from analysis of explosions. Imparted velocity distributions are computed using a shock-physics code to model the explosions within the launch vehicle. A comparison of the debris catalog with an existing catalog for the Shuttle external tank show good comparison in the debris characteristics and the predicted debris strike probability. The model is used to analyze the effects of number of debris pieces and velocity distributions on the strike probability and risk.

Initial Risk Analysis and Decision Making Framework

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

Engel, David W.

2012-02-01

Commercialization of new carbon capture simulation initiative (CCSI) technology will include two key elements of risk management, namely, technical risk (will process and plant performance be effective, safe, and reliable) and enterprise risk (can project losses and costs be controlled within the constraints of market demand to maintain profitability and investor confidence). Both of these elements of risk are incorporated into the risk analysis subtask of Task 7. Thus far, this subtask has developed a prototype demonstration tool that quantifies risk based on the expected profitability of expenditures when retrofitting carbon capture technology on a stylized 650 MW pulverized coalmore » electric power generator. The prototype is based on the selection of specific technical and financial factors believed to be important determinants of the expected profitability of carbon capture, subject to uncertainty. The uncertainty surrounding the technical performance and financial variables selected thus far is propagated in a model that calculates the expected profitability of investments in carbon capture and measures risk in terms of variability in expected net returns from these investments. Given the preliminary nature of the results of this prototype, additional work is required to expand the scope of the model to include additional risk factors, additional information on extant and proposed risk factors, the results of a qualitative risk factor elicitation process, and feedback from utilities and other interested parties involved in the carbon capture project. Additional information on proposed distributions of these risk factors will be integrated into a commercial implementation framework for the purpose of a comparative technology investment analysis.« less

Risk modelling study for carotid endarterectomy.

PubMed

Kuhan, G; Gardiner, E D; Abidia, A F; Chetter, I C; Renwick, P M; Johnson, B F; Wilkinson, A R; McCollum, P T

2001-12-01

The aims of this study were to identify factors that influence the risk of stroke or death following carotid endarterectomy (CEA) and to develop a model to aid in comparative audit of vascular surgeons and units. A series of 839 CEAs performed by four vascular surgeons between 1992 and 1999 was analysed. Multiple logistic regression analysis was used to model the effect of 15 possible risk factors on the 30-day risk of stroke or death. Outcome was compared for four surgeons and two units after adjustment for the significant risk factors. The overall 30-day stroke or death rate was 3.9 per cent (29 of 741). Heart disease, diabetes and stroke were significant risk factors. The 30-day predicted stroke or death rates increased with increasing risk scores. The observed 30-day stroke or death rate was 3.9 per cent for both vascular units and varied from 3.0 to 4.2 per cent for the four vascular surgeons. Differences in the outcomes between the surgeons and vascular units did not reach statistical significance after risk adjustment. Diabetes, heart disease and stroke are significant risk factors for stroke or death following CEA. The risk score model identified patients at higher risk and aided in comparative audit.

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study.

PubMed

AbdelRahman, Samir E; Zhang, Mingyuan; Bray, Bruce E; Kawamoto, Kensaku

2014-05-27

The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time. Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak. The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%. The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

Simulation for Prediction of Entry Article Demise (SPEAD): An Analysis Tool for Spacecraft Safety Analysis and Ascent/Reentry Risk Assessment

NASA Technical Reports Server (NTRS)

Ling, Lisa

2014-01-01

For the purpose of performing safety analysis and risk assessment for a potential off-nominal atmospheric reentry resulting in vehicle breakup, a synthesis of trajectory propagation coupled with thermal analysis and the evaluation of node failure is required to predict the sequence of events, the timeline, and the progressive demise of spacecraft components. To provide this capability, the Simulation for Prediction of Entry Article Demise (SPEAD) analysis tool was developed. The software and methodology have been validated against actual flights, telemetry data, and validated software, and safety/risk analyses were performed for various programs using SPEAD. This report discusses the capabilities, modeling, validation, and application of the SPEAD analysis tool.

Topography- and nightlight-based national flood risk assessment in Canada

NASA Astrophysics Data System (ADS)

Elshorbagy, Amin; Bharath, Raja; Lakhanpal, Anchit; Ceola, Serena; Montanari, Alberto; Lindenschmidt, Karl-Erich

2017-04-01

In Canada, flood analysis and water resource management, in general, are tasks conducted at the provincial level; therefore, unified national-scale approaches to water-related problems are uncommon. In this study, a national-scale flood risk assessment approach is proposed and developed. The study focuses on using global and national datasets available with various resolutions to create flood risk maps. First, a flood hazard map of Canada is developed using topography-based parameters derived from digital elevation models, namely, elevation above nearest drainage (EAND) and distance from nearest drainage (DFND). This flood hazard mapping method is tested on a smaller area around the city of Calgary, Alberta, against a flood inundation map produced by the city using hydraulic modelling. Second, a flood exposure map of Canada is developed using a land-use map and the satellite-based nightlight luminosity data as two exposure parameters. Third, an economic flood risk map is produced, and subsequently overlaid with population density information to produce a socioeconomic flood risk map for Canada. All three maps of hazard, exposure, and risk are classified into five classes, ranging from very low to severe. A simple way to include flood protection measures in hazard estimation is also demonstrated using the example of the city of Winnipeg, Manitoba. This could be done for the entire country if information on flood protection across Canada were available. The evaluation of the flood hazard map shows that the topography-based method adopted in this study is both practical and reliable for large-scale analysis. Sensitivity analysis regarding the resolution of the digital elevation model is needed to identify the resolution that is fine enough for reliable hazard mapping, but coarse enough for computational tractability. The nightlight data are found to be useful for exposure and risk mapping in Canada; however, uncertainty analysis should be conducted to investigate the effect of the overglow phenomenon on flood risk mapping.

The Validity and Utility of the California Family Risk Assessment under Practice Conditions in the Field: A Prospective Study

ERIC Educational Resources Information Center

Johnson, Will L.

2011-01-01

Objective: Analysis of the validity and implementation of a child maltreatment actuarial risk assessment model, the California Family Risk Assessment (CFRA). Questions addressed: (1) Is there evidence of the validity of the CFRA under field operating conditions? (2) Do actuarial risk assessment results influence child welfare workers' service…

Combined Patterns of Risk for Problem and Obesogenic Behaviors in Adolescents: A Latent Class Analysis Approach

ERIC Educational Resources Information Center

Fleary, Sasha A.

2017-01-01

Background: Several studies have used latent class analyses to explore obesogenic behaviors and substance use in adolescents independently. We explored a variety of health risks jointly to identify distinct patterns of risk behaviors among adolescents. Methods: Latent class models were estimated using Youth Risk Behavior Surveillance System…

Association of colorectal cancer susceptibility variants with esophageal cancer in a Chinese population.

PubMed

Geng, Ting-Ting; Xun, Xiao-Jie; Li, Sen; Feng, Tian; Wang, Li-Ping; Jin, Tian-Bo; Hou, Peng

2015-06-14

To investigate the association between colorectal cancer (CRC) genetic susceptibility variants and esophageal cancer in a Chinese Han population. A case-control study was conducted including 360 esophageal cancer patients and 310 healthy controls. Thirty-one single-nucleotide polymorphisms (SNPs) associated with CRC risk from previous genome-wide association studies were analyzed. SNPs were genotyped using Sequenom Mass-ARRAY technology, and genotypic frequencies in controls were tested for departure from Hardy-Weinberg equilibrium using a Fisher's exact test. The allelic frequencies were compared between cases and controls using a χ(2) test. Associations between the SNPs and the risk of esophageal cancer were tested using various genetic models (codominant, dominant, recessive, overdominant, and additive). ORs and 95%CIs were calculated by unconditional logistic regression with adjustments for age and sex. The minor alleles of rs1321311 and rs4444235 were associated with a 1.53-fold (95%CI: 1.15-2.06; P = 0.004) and 1.28-fold (95%CI: 1.03-1.60; P = 0.028) increased risk of esophageal cancer in the allelic model analysis, respectively. In the genetic model analysis, the C/C genotype of rs3802842 was associated with a reduced risk of esophageal cancer in the codominant model (OR = 0.52, 95%CI: 0.31-0.88; P = 0.033) and recessive model (OR = 0.55, 95%CI: 0.34-0.87; P = 0.010). The rs4939827 C/T-T/T genotype was associated with a 0.67-fold (95%CI: 0.46-0.98; P = 0.038) decreased esophageal cancer risk under the dominant model. In addition, rs6687758, rs1321311, and rs4444235 were associated with an increased risk. In particular, the T/T genotype of rs1321311 was associated with an 8.06-fold (95%CI: 1.96-33.07; P = 0.004) increased risk in the codominant model. These results provide evidence that known genetic variants associated with CRC risk confer risk for esophageal cancer, and may bring risk for other digestive system tumors.

Intelligent Adversary Risk Analysis: A Bioterrorism Risk Management Model (PREPRINT)

DTIC Science & Technology

2009-02-20

Dengue • Listeria monocytogenes • Filoviruses coronavirus ( SARS -CoV) • Campylobacter jejuni • Ebola • Yersinia enterocolitica) • Marburg...OF: 17. LIMITATION OF ABSTRACT Same as Report ( SAR ) 18. NUMBER OF PAGES 29 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT...which may be to maximize the consequences they can inflict (Golany et al., Figure 2: Decision Tree Example  7    Submitted to Risk Analysis

PACE and the Medicare+Choice risk-adjusted payment model.

PubMed

Temkin-Greener, H; Meiners, M R; Gruenberg, L

2001-01-01

This paper investigates the impact of the Medicare principal inpatient diagnostic cost group (PIP-DCG) payment model on the Program of All-Inclusive Care for the Elderly (PACE). Currently, more than 6,000 Medicare beneficiaries who are nursing home certifiable receive care from PACE, a program poised for expansion under the Balanced Budget Act of 1997. Overall, our analysis suggests that the application of the PIP-DCG model to the PACE program would reduce Medicare payments to PACE, on average, by 38%. The PIP-DCG payment model bases its risk adjustment on inpatient diagnoses and does not capture adequately the risk of caring for a population with functional impairments.

Bite the apple, get driven out of the garden: A risky story telling at the ASME town meeting

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

Majumdar, K.C.

1994-11-01

Risk, the all-encompassing four-letter word became a widely used household cliche and an institutional mantra in the nineties. Risk analysis models from the Garden of Eden to the Capitol Hill lawn have made a number of sharp paradigm shifts to evolve itself as a decision-making tool from individual risk perception to societal risk-based regulatory media. Risk always coexists with benefit and is arbitrated by costs. Risk-benefit analysis has been in use in business and industry in economic ventures for a long time. Only recently risk management in its current state of development, evolved as a regulatory tool for controlling largemore » technological systems that have potential impacts on the health and safety of the public and on the sustainability of the ecology and the environment. This paper summarizes the evolution of the risk management concepts and models in industry and the regulatory agencies in the US over the last three decades. It also discusses the benefits and limitations of this evolving discipline as it is applied to high-risk technologies from the nuclear power plant and petrochemical industry, etc. to nuclear weapons technology.« less

A Meta-Analysis and Multisite Time-Series Analysis of the Differential Toxicity of Major Fine Particulate Matter Constituents

PubMed Central

Levy, Jonathan I.; Diez, David; Dou, Yiping; Barr, Christopher D.; Dominici, Francesca

2012-01-01

Health risk assessments of particulate matter less than 2.5 μm in diameter (PM2.5) often assume that all constituents of PM2.5 are equally toxic. While investigators in previous epidemiologic studies have evaluated health risks from various PM2.5 constituents, few have conducted the analyses needed to directly inform risk assessments. In this study, the authors performed a literature review and conducted a multisite time-series analysis of hospital admissions and exposure to PM2.5 constituents (elemental carbon, organic carbon matter, sulfate, and nitrate) in a population of 12 million US Medicare enrollees for the period 2000–2008. The literature review illustrated a general lack of multiconstituent models or insight about probabilities of differential impacts per unit of concentration change. Consistent with previous results, the multisite time-series analysis found statistically significant associations between short-term changes in elemental carbon and cardiovascular hospital admissions. Posterior probabilities from multiconstituent models provided evidence that some individual constituents were more toxic than others, and posterior parameter estimates coupled with correlations among these estimates provided necessary information for risk assessment. Ratios of constituent toxicities, commonly used in risk assessment to describe differential toxicity, were extremely uncertain for all comparisons. These analyses emphasize the subtlety of the statistical techniques and epidemiologic studies necessary to inform risk assessments of particle constituents. PMID:22510275

Risk Analysis of Earth-Rock Dam Failures Based on Fuzzy Event Tree Method

PubMed Central

Fu, Xiao; Gu, Chong-Shi; Su, Huai-Zhi; Qin, Xiang-Nan

2018-01-01

Earth-rock dams make up a large proportion of the dams in China, and their failures can induce great risks. In this paper, the risks associated with earth-rock dam failure are analyzed from two aspects: the probability of a dam failure and the resulting life loss. An event tree analysis method based on fuzzy set theory is proposed to calculate the dam failure probability. The life loss associated with dam failure is summarized and refined to be suitable for Chinese dams from previous studies. The proposed method and model are applied to one reservoir dam in Jiangxi province. Both engineering and non-engineering measures are proposed to reduce the risk. The risk analysis of the dam failure has essential significance for reducing dam failure probability and improving dam risk management level. PMID:29710824

Optimal security investments and extreme risk.

PubMed

Mohtadi, Hamid; Agiwal, Swati

2012-08-01

In the aftermath of 9/11, concern over security increased dramatically in both the public and the private sector. Yet, no clear algorithm exists to inform firms on the amount and the timing of security investments to mitigate the impact of catastrophic risks. The goal of this article is to devise an optimum investment strategy for firms to mitigate exposure to catastrophic risks, focusing on how much to invest and when to invest. The latter question addresses the issue of whether postponing a risk mitigating decision is an optimal strategy or not. Accordingly, we develop and estimate both a one-period model and a multiperiod model within the framework of extreme value theory (EVT). We calibrate these models using probability measures for catastrophic terrorism risks associated with attacks on the food sector. We then compare our findings with the purchase of catastrophic risk insurance. © 2012 Society for Risk Analysis.

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.

Path analysis of risk factors leading to premature birth.

PubMed

Fields, S J; Livshits, G; Sirotta, L; Merlob, P

1996-01-01

The present study tested whether various sociodemographic, anthropometric, behavioral, and medical/physiological factors act in a direct or indirect manner on the risk of prematurity using path analysis on a sample of Israeli births. The path model shows that medical complications, primarily toxemia, chorioammionitis, and a previous low birth weight delivery directly and significantly act on the risk of prematurity as do low maternal pregnancy weight gain and ethnicity. Other medical complications, including chronic hypertension, preclampsia, and placental abruption, although significantly correlated with prematurity, act indirectly on prematurity through toxemia. The model further shows that the commonly accepted sociodemographic, anthropometric, and behavioral risk factors act by modifying the development of medical complications that lead to prematurity as opposed to having a direct effect on premature delivery. © 1996 Wiley-Liss, Inc. Copyright © 1996 Wiley-Liss, Inc.

Using Latent Class Analysis to Identify Academic and Behavioral Risk Status in Elementary Students

ERIC Educational Resources Information Center

King, Kathleen R.; Lembke, Erica S.; Reinke, Wendy M.

2016-01-01

Identifying classes of children on the basis of academic and behavior risk may have important implications for the allocation of intervention resources within Response to Intervention (RTI) and Multi-Tiered System of Support (MTSS) models. Latent class analysis (LCA) was conducted with a sample of 517 third grade students. Fall screening scores in…

Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

PubMed Central

Pennells, Lisa; Kaptoge, Stephen; White, Ian R.; Thompson, Simon G.; Wood, Angela M.; Tipping, Robert W.; Folsom, Aaron R.; Couper, David J.; Ballantyne, Christie M.; Coresh, Josef; Goya Wannamethee, S.; Morris, Richard W.; Kiechl, Stefan; Willeit, Johann; Willeit, Peter; Schett, Georg; Ebrahim, Shah; Lawlor, Debbie A.; Yarnell, John W.; Gallacher, John; Cushman, Mary; Psaty, Bruce M.; Tracy, Russ; Tybjærg-Hansen, Anne; Price, Jackie F.; Lee, Amanda J.; McLachlan, Stela; Khaw, Kay-Tee; Wareham, Nicholas J.; Brenner, Hermann; Schöttker, Ben; Müller, Heiko; Jansson, Jan-Håkan; Wennberg, Patrik; Salomaa, Veikko; Harald, Kennet; Jousilahti, Pekka; Vartiainen, Erkki; Woodward, Mark; D'Agostino, Ralph B.; Bladbjerg, Else-Marie; Jørgensen, Torben; Kiyohara, Yutaka; Arima, Hisatomi; Doi, Yasufumi; Ninomiya, Toshiharu; Dekker, Jacqueline M.; Nijpels, Giel; Stehouwer, Coen D. A.; Kauhanen, Jussi; Salonen, Jukka T.; Meade, Tom W.; Cooper, Jackie A.; Cushman, Mary; Folsom, Aaron R.; Psaty, Bruce M.; Shea, Steven; Döring, Angela; Kuller, Lewis H.; Grandits, Greg; Gillum, Richard F.; Mussolino, Michael; Rimm, Eric B.; Hankinson, Sue E.; Manson, JoAnn E.; Pai, Jennifer K.; Kirkland, Susan; Shaffer, Jonathan A.; Shimbo, Daichi; Bakker, Stephan J. L.; Gansevoort, Ron T.; Hillege, Hans L.; Amouyel, Philippe; Arveiler, Dominique; Evans, Alun; Ferrières, Jean; Sattar, Naveed; Westendorp, Rudi G.; Buckley, Brendan M.; Cantin, Bernard; Lamarche, Benoît; Barrett-Connor, Elizabeth; Wingard, Deborah L.; Bettencourt, Richele; Gudnason, Vilmundur; Aspelund, Thor; Sigurdsson, Gunnar; Thorsson, Bolli; Kavousi, Maryam; Witteman, Jacqueline C.; Hofman, Albert; Franco, Oscar H.; Howard, Barbara V.; Zhang, Ying; Best, Lyle; Umans, Jason G.; Onat, Altan; Sundström, Johan; Michael Gaziano, J.; Stampfer, Meir; Ridker, Paul M.; Michael Gaziano, J.; Ridker, Paul M.; Marmot, Michael; Clarke, Robert; Collins, Rory; Fletcher, Astrid; Brunner, Eric; Shipley, Martin; Kivimäki, Mika; Ridker, Paul M.; Buring, Julie; Cook, Nancy; Ford, Ian; Shepherd, James; Cobbe, Stuart M.; Robertson, Michele; Walker, Matthew; Watson, Sarah; Alexander, Myriam; Butterworth, Adam S.; Angelantonio, Emanuele Di; Gao, Pei; Haycock, Philip; Kaptoge, Stephen; Pennells, Lisa; Thompson, Simon G.; Walker, Matthew; Watson, Sarah; White, Ian R.; Wood, Angela M.; Wormser, David; Danesh, John

2014-01-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous. PMID:24366051

Assessing risk prediction models using individual participant data from multiple studies.

PubMed

Pennells, Lisa; Kaptoge, Stephen; White, Ian R; Thompson, Simon G; Wood, Angela M

2014-03-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

Association of RTEL1 gene polymorphisms with stroke risk in a Chinese Han population.

PubMed

Cai, Yi; Zeng, Chaosheng; Su, Qingjie; Zhou, Jingxia; Li, Pengxiang; Dai, Mingming; Wang, Desheng; Long, Faqing

2017-12-29

We investigated the associations between single nucleotide polymorphisms (SNPs) in the regulator of telomere elongation helicase 1 ( RTEL1 ) gene and stroke in the Chinese population. A total of 400 stroke patients and 395 healthy participants were included in this study. Five SNPs in RTEL1 were genotyped and the association with stroke risk was analyzed. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using unconditional logistic regression analysis. Multivariate logistic regression analysis was used to identify SNPs that correlated with stroke. Rs2297441 was associated with an increased risk of stroke in an allele model (odds ratio [OR] = 1.24, 95% confidence interval [95% CI] = 1.01-1.52, p = 0.043). Rs6089953 was associated with an increased risk of stroke under the genotype model ([OR] = 1.862, [CI] = 1.123-3.085, p = 0.016). Rs2297441 was associated with an increased risk of stroke in an additive model (OR = 1.234, 95% CI = 1.005, p = 0.045, Rs6089953, Rs6010620 and Rs6010621 were associated with an increased risk of stroke in the recessive model (Rs6089953:OR = 1.825, 95% CI = 1.121-2.969, p =0.01546; Rs6010620: OR = 1.64, 95% CI = 1.008-2.669, p =0.04656;Rs6010621:OR = 1.661, 95% CI = 1.014-2.722, p =0.04389). Our findings reveal a possible association between SNPs in the RTEL1 gene and stroke risk in Chinese population.

Appalachian Basin Play Fairway Analysis: Thermal Quality Analysis in Low-Temperature Geothermal Play Fairway Analysis (GPFA-AB

DOE Data Explorer

Teresa E. Jordan

2015-11-15

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB, DOE Project DE-EE0006726). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria or ‘risks’: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS). This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain “item description” documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations. Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015. Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted.

Evaluating the risk of water distribution system failure: A shared frailty model

NASA Astrophysics Data System (ADS)

Clark, Robert M.; Thurnau, Robert C.

2011-12-01

Condition assessment (CA) Modeling is drawing increasing interest as a technique that can assist in managing drinking water infrastructure. This paper develops a model based on the application of a Cox proportional hazard (PH)/shared frailty model and applies it to evaluating the risk of failure in drinking water networks using data from the Laramie Water Utility (located in Laramie, Wyoming, USA). Using the risk model a cost/ benefit analysis incorporating the inspection value method (IVM), is used to assist in making improved repair, replacement and rehabilitation decisions for selected drinking water distribution system pipes. A separate model is developed to predict failures in prestressed concrete cylinder pipe (PCCP). Various currently available inspection technologies are presented and discussed.

[Organizational and management companies models].

PubMed

Tomei, G; Tomei, F; Fiaschetti, M; De Sio, S; Tria, M; Schifano, M P; Monti, C; Tasciotti, Z; Panfili, T; Caciari, A; Sancini, A

2010-01-01

With the legislative decree 81/08 and s.m.i. it's explicitly defined a model of management and corporate organization that can contribute to prevent security risks in work environments. The realization of the model is not obligatory, but desirable because the result of its implementation is a decrease of company's risks and costs for safety. Our study group has developed the structure of an organizational and management model for corporate safety and the tools necessary for its realization. The realization of a model is structured in various phases: initial exam, safety policy, planification, implementation, monitoring, system retest and improvement. Such a model, in continuous evolution, is based on the responsibilities of the different corporate figures through an accurate analysis of the measured risks and the measures adopted.

Using the Integrated Behavioral Model to Predict High-Risk Drinking among College Students

ERIC Educational Resources Information Center

Braun, Robert E.; Glassman, Tavis; Sheu, Jiunn-Jye; Dake, Joseph; Jordan, Tim; Yingling, Faith

2014-01-01

This study assessed the Integrated Behavioral Model's (IBM) utility in explaining high-risk drinking among college students. A total of 356 participants completed a four-page questionnaire based on the (IBM) theory and their drinking behavior. The results from a path analysis revealed three significant constructs (p<0.05) which predicted…

APPLICATION AND EVALUATION OF AN AGGREGATE PHYSICALLY-BASED TWO-STAGE MONTE CARLO PROBABILISTIC MODEL FOR QUANTIFYING CHILDREN'S RESIDENTIAL EXPOSURE AND DOSE TO CHLORPYRIFOS

EPA Science Inventory

Critical voids in exposure data and models lead risk assessors to rely on conservative assumptions. Risk assessors and managers need improved tools beyond the screening level analysis to address aggregate exposures to pesticides as required by the Food Quality Protection Act o...

Overview of the Special Issue: A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

EPA Science Inventory

The Climate Change Impacts and Risk Analysis (CIRA) project establishes a new multi-model framework to systematically assess the impacts, economic damages, and risks from climate change in the United States. The primary goal of this framework to estimate how climate change impac...

Application of wildfire simulation models for risk analysis

Treesearch

Alan A. Ager; Mark A. Finney

2009-01-01

Wildfire simulation models are being widely used by fire and fuels specialists in the U.S. to support tactical and strategic decisions related to the mitigation of wildfire risk. Much of this application has resulted from the development of a minimum travel time (MTT) fire spread algorithm (M. Finney) that makes it computationally feasible to simulate thousands of...

Assessment of possible airborne impact from nuclear risk sites - Part II: probabilistic analysis of atmospheric transport patterns in Euro-Arctic region

NASA Astrophysics Data System (ADS)

Mahura, A. G.; Baklanov, A. A.

2003-10-01

The probabilistic analysis of atmospheric transport patterns from most important nuclear risk sites in the Euro-Arctic region is performed employing the methodology developed within the "Arctic Risk" Project of the NARP Programme (Baklanov and Mahura, 2003). The risk sites are the nuclear power plants in the Northwest Russia, Finland, Sweden, Lithuania, United Kingdom, and Germany as well as the Novaya Zemlya test site of Russia. The geographical regions of interest are the Northern and Central European countries and Northwest Russia. In this study, the employed research tools are the trajectory model to calculate a multiyear dataset of forward trajectories that originated over the risk site locations, and a set of statistical methods (including exploratory, cluster, and probability fields analyses) for analysis of trajectory modelling results. The probabilistic analyses of trajectory modelling results for eleven sites are presented as a set of various indicators of the risk sites possible impact on geographical regions and countries of interest. The nuclear risk site possible impact (on a particular geographical region, territory, country, site, etc.) due to atmospheric transport from the site after hypothetical accidental release of radioactivity can be properly estimated based on a combined interpretation of the indicators (simple characteristics, atmospheric transport pathways, airflow and fast transport probability fields, maximum reaching distance and maximum possible impact zone, typical transport time and precipitation factor fields) for different time periods (annual, seasonal, and monthly) for any selected site (both separately for each site or grouped for several sites) in the Euro-Arctic region. Such estimation could be the useful input information for the decision-making process, risk assessment, and planning of emergency response systems for sites of nuclear, chemical, and biological danger.

Alcohol consumption and all-cause mortality.

PubMed

Duffy, J C

1995-02-01

Prospective studies of alcohol and mortality in middle-aged men almost universally find a U-shaped relationship between alcohol consumption and risk of mortality. This review demonstrates the extent to which different studies lead to different risk estimates, analyses the putative influence of abstention as a risk factor and uses available data to produce point and interval estimates of the consumption level apparently associated with minimum risk from two studies in the UK. Data from a number of studies are analysed by means of logistic-linear modelling, taking account of the possible influence of abstention as a special risk factor. Separate analysis of British data is performed. Logistic-linear modelling demonstrates large and highly significant differences between the studies considered in the relationship between alcohol consumption and all-cause mortality. The results support the identification of abstention as a special risk factor for mortality, but do not indicate that this alone explains the apparent U-shaped relationship. Separate analysis of two British studies indicates minimum risk of mortality in this population at a consumption level of about 26 (8.5 g) units of alcohol per week. The analysis supports the view that abstention may be a specific risk factor for all-cause mortality, but is not an adequate explanation of the apparent protective effect of alcohol consumption against all-cause mortality. Future analyses might better be performed on a case-by-case basis, using a change-point model to estimate the parameters of the relationship. The current misinterpretation of the sensible drinking level of 21 units per week for men in the UK as a limit is not justified, and the data suggest that alcohol consumption is a net preventive factor against premature death in this population.

An Overview of NASA's Oribital Debris Environment Model

NASA Technical Reports Server (NTRS)

Matney, Mark

2010-01-01

Using updated measurement data, analysis tools, and modeling techniques; the NASA Orbital Debris Program Office has created a new Orbital Debris Environment Model. This model extends the coverage of orbital debris flux throughout the Earth orbit environment, and includes information on the mass density of the debris as well as the uncertainties in the model environment. This paper will give an overview of this model and its implications for spacecraft risk analysis.

A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome.

PubMed

Ambler, Gareth; Omar, Rumana Z; Royston, Patrick

2007-06-01

Risk models that aim to predict the future course and outcome of disease processes are increasingly used in health research, and it is important that they are accurate and reliable. Most of these risk models are fitted using routinely collected data in hospitals or general practices. Clinical outcomes such as short-term mortality will be near-complete, but many of the predictors may have missing values. A common approach to dealing with this is to perform a complete-case analysis. However, this may lead to overfitted models and biased estimates if entire patient subgroups are excluded. The aim of this paper is to investigate a number of methods for imputing missing data to evaluate their effect on risk model estimation and the reliability of the predictions. Multiple imputation methods, including hotdecking and multiple imputation by chained equations (MICE), were investigated along with several single imputation methods. A large national cardiac surgery database was used to create simulated yet realistic datasets. The results suggest that complete case analysis may produce unreliable risk predictions and should be avoided. Conditional mean imputation performed well in our scenario, but may not be appropriate if using variable selection methods. MICE was amongst the best performing multiple imputation methods with regards to the quality of the predictions. Additionally, it produced the least biased estimates, with good coverage, and hence is recommended for use in practice.

Myopia and/or longer axial length are protective against diabetic retinopathy: a meta-analysis.

PubMed

Fu, Yu; Geng, Dengfeng; Liu, Hua; Che, Huixin

2016-06-01

To evaluate the current evidence of the relationship between myopia, together with its structural and refractive component, and diabetic retinopathy (DR) risk. A systematic search was performed up to April, 2015. Summary odds ratios (ORs) and 95% confidence intervals (CIs) were calculated employing random-effects models. Three models were used to assess the association between myopia and risk of DR: axial length (AL) (per millimetre increase) and DR; myopia (myopia versus non-myopia) and DR; refractive error (RE) (per D decrease) and DR. Publication bias of the literature was evaluated using Begg's funnel plots and Egger's test. A total of 11 studies that met the predefined criteria were included in this meta-analysis. Overall, longer AL (per millimetre increase) was associa-ted with a significantly decreased risk of DR (combined OR, 0.75; 95% CI, 0.65-0.86; p < 0.001); myopic eyes (myopia versus non-myopia) showed a lower risk of DR (combined OR, 0.70; 95% CI, 0.58-0.85; p < 0.001). A greater degree of myopic RE (per D decrease) also revealed a significantly decreased risk of DR (combined OR, 0.89; 95% CI, 0.85-0.93; p < 0.001). The sensitivity analyses and cumulative meta-analysis showed similar results. No publication bias was detected in any of the three models. This meta-analysis suggests that both myopic refraction and longer AL are associated with a lower risk of DR. Further studies are needed to determine exact mechanisms underpinning the protective effect of myopia against DR. © 2015 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Interaction between nonsynonymous polymorphisms in PLA2G7 gene and smoking on the risk of coronary heart disease in a Chinese population.

PubMed

Chi, Yunpeng; Shi, Conghong; Zhang, Xiaojiang; Xi, Yang

2018-05-04

To investigate the impact of PLA2G7 polymorphism, and additional their interactions with smoking and drinking on coronary heart disease (CHD) risk based on Chinese population. GMDR model was used to screen the best gene-smoking and gene-drinking interaction combinations. Logistic regression was performed to investigate association between 4 SNPs and CHD, and the interaction effect between rs1805017 and smoking. For CHD patient-control haplotype analyses, the SHEsis online haplotype analysis software ( http://analysis.bio-x.cn/myAnalysis.php ) was employed. CHD risks were higher in carriers of homozygous mutant of rs1805017 and rs1805018 than those with wild-type homozygotes, OR (95% CI) were 1.45 (1.16-1.92) and 1.51 (1.23-1.97), respectively, but the other two SNPs, rs16874954 and rs1051931 were not significant associated with CHD risks. GMDR analysis indicated that there was a significant two-locus model (p = 0.0107) involving rs1805017 and smoking, indicating a potential gene-environment interaction between rs1805017 and smoking. But we did not found any gene-drinking and gene-gene interaction combinations in GMDR models. The haplotype R-I was observed most frequently in two groups, with 47.43 and 54.38% in the case and control group of the population, respectively. The results also indicated that the haplotype containing the rs1805017-H and rs1805018-T alleles were associated with a statistically increased CHD risk, OR (95% CI) 1.43 (1.10-1.86), p = 0.0021. Polymorphisms in rs1805017 and rs1805018, additional interaction between rs1805017 and smoking, and haplotype containing the rs1805017-H and rs1805018-T alleles were associated with increased CHD risk.

Life history theory and breast cancer risk: methodological and theoretical challenges: Response to "Is estrogen receptor negative breast cancer risk associated with a fast life history strategy?".

PubMed

Aktipis, Athena

2016-01-01

In a meta-analysis published by myself and co-authors, we report differences in the life history risk factors for estrogen receptor negative (ER-) and estrogen receptor positive (ER+) breast cancers. Our meta-analysis did not find the association of ER- breast cancer risk with fast life history characteristics that Hidaka and Boddy suggest in their response to our article. There are a number of possible explanations for the differences between their conclusions and the conclusions we drew from our meta-analysis, including limitations of our meta-analysis and methodological challenges in measuring and categorizing estrogen receptor status. These challenges, along with the association of ER+ breast cancer with slow life history characteristics, may make it challenging to find a clear signal of ER- breast cancer with fast life history characteristics, even if that relationship does exist. The contradictory results regarding breast cancer risk and life history characteristics illustrate a more general challenge in evolutionary medicine: often different sub-theories in evolutionary biology make contradictory predictions about disease risk. In this case, life history models predict that breast cancer risk should increase with faster life history characteristics, while the evolutionary mismatch hypothesis predicts that breast cancer risk should increase with delayed reproduction. Whether life history tradeoffs contribute to ER- breast cancer is still an open question, but current models and several lines of evidence suggest that it is a possibility. © The Author(s) 2016. Published by Oxford University Press on behalf of the Foundation for Evolution, Medicine, and Public Health.

Vitamin E and risk of age-related cataract: a meta-analysis.

PubMed

Zhang, Yufei; Jiang, Wenjie; Xie, Zhutian; Wu, Wenlong; Zhang, Dongfeng

2015-10-01

We conducted a meta-analysis to evaluate the relationship between vitamin E and age-related cataract (ARC). The fixed- or random-effect model was selected based on heterogeneity. Meta-regression was used to explore potential sources of between-study heterogeneity. Publication bias was evaluated using Begg's test. The dose-response relationship was assessed by a restricted cubic spline model. Relevant studies were identified by a search of PubMed and the Cochrane Library to May 2014, without language restrictions. Studies involved samples of people of all ages. Dietary vitamin E intake, dietary and supplemental vitamin E intake, and high serum tocopherol levels were significantly associated with decreased risk of ARC, the pooled relative risk was 0·73 (95% CI 0·58, 0·92), 0·86 (95% CI 0·75, 0·99) and 0·77 (95% CI 0·66, 0·91), respectively. Supplemental vitamin E intake was non-significantly associated with ARC risk (relative risk=0·92; 95% CI 0·78, 1·07). The findings from dose-response analysis showed evidence of a non-linear association between dietary vitamin E intake and ARC. The risk of ARC decreased with dietary vitamin E intake from 7 mg/d (relative risk=0·94; 95% CI 0·90, 0·97). The findings of the meta-analysis indicated that dietary vitamin E intake, dietary and supplemental vitamin E intake, and high level of serum tocopherol might be significantly associated with reduced ARC risk.

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.

Single-nucleotide polymorphisms of MMP2 in MMP/TIMP pathways associated with the risk of alcohol-induced osteonecrosis of the femoral head in Chinese males: A case-control study.

PubMed

Yu, Yan; Xie, Zhilan; Wang, Jihan; Chen, Chu; Du, Shuli; Chen, Peng; Li, Bin; Jin, Tianbo; Zhao, Heping

2016-12-01

The proportion of alcohol-induced osteonecrosis of the femoral head (ONFH) in all ONFH patients was 30.7%, with males prevailing among the ONFH patients in mainland China (70.1%). Matrix metalloproteinase 2 (MMP2), a member of the MMP gene family, encodes the enzyme MMP2, which can promote osteoclast migration, attachment, and bone matrix degradation. In this case-control study, we aimed to investigate the association between MMP2 and the alcohol-induced ONFH in Chinese males.In total, 299 patients with alcohol-induced ONFH and 396 healthy controls were recruited for a case-control association study. Five single-nucleotide polymorphisms within the MMP2 locus were genotyped and examined for their correlation with the risk of alcohol-induced ONFH and treatment response using Pearson χ test and unconditional logistic regression analysis. We identified 3 risk alleles for carriers: the allele "T" of rs243849 increased the risk of alcohol-induced ONFH in the allele model, the log-additive model without adjustment, and the log-additive model with adjustment for age. Conversely, the genotypes "CC" in rs7201 and "CC" in rs243832 decreased the risk of alcohol-induced ONFH, as revealed by the recessive model. After the Bonferroni multiple adjustment, no significant association was found. Furthermore, the haplotype analysis showed that the "TT" haplotype of MMP2 was more frequent among patients with alcohol-induced ONFH by unconditional logistic regression analysis adjusted for age.In conclusion, there may be an association between MMP2 and the risk of alcohol-induced ONFH in North-Chinese males. However, studies on larger populations are needed to confirm this hypothesis; these data may provide a theoretical foundation for future studies.

A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis.

PubMed

Tervonen, Tommi; van Valkenhoef, Gert; Buskens, Erik; Hillege, Hans L; Postmus, Douwe

2011-05-30

Drug benefit-risk (BR) analysis is based on firm clinical evidence regarding various safety and efficacy outcomes. In this paper, we propose a new and more formal approach for constructing a supporting multi-criteria model that fully takes into account the evidence on efficacy and adverse drug reactions. Our approach is based on the stochastic multi-criteria acceptability analysis methodology, which allows us to compute the typical value judgments that support a decision, to quantify decision uncertainty, and to compute a comprehensive BR profile. We construct a multi-criteria model for the therapeutic group of second-generation antidepressants. We assess fluoxetine and venlafaxine together with placebo according to incidence of treatment response and three common adverse drug reactions by using data from a published study. Our model shows that there are clear trade-offs among the treatment alternatives. Copyright © 2011 John Wiley & Sons, Ltd.

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.

Adaptive servo ventilation for central sleep apnoea in heart failure: SERVE-HF on-treatment analysis.

PubMed

Woehrle, Holger; Cowie, Martin R; Eulenburg, Christine; Suling, Anna; Angermann, Christiane; d'Ortho, Marie-Pia; Erdmann, Erland; Levy, Patrick; Simonds, Anita K; Somers, Virend K; Zannad, Faiez; Teschler, Helmut; Wegscheider, Karl

2017-08-01

This on-treatment analysis was conducted to facilitate understanding of mechanisms underlying the increased risk of all-cause and cardiovascular mortality in heart failure patients with reduced ejection fraction and predominant central sleep apnoea randomised to adaptive servo ventilation versus the control group in the SERVE-HF trial.Time-dependent on-treatment analyses were conducted (unadjusted and adjusted for predictive covariates). A comprehensive, time-dependent model was developed to correct for asymmetric selection effects (to minimise bias).The comprehensive model showed increased cardiovascular death hazard ratios during adaptive servo ventilation usage periods, slightly lower than those in the SERVE-HF intention-to-treat analysis. Self-selection bias was evident. Patients randomised to adaptive servo ventilation who crossed over to the control group were at higher risk of cardiovascular death than controls, while control patients with crossover to adaptive servo ventilation showed a trend towards lower risk of cardiovascular death than patients randomised to adaptive servo ventilation. Cardiovascular risk did not increase as nightly adaptive servo ventilation usage increased.On-treatment analysis showed similar results to the SERVE-HF intention-to-treat analysis, with an increased risk of cardiovascular death in heart failure with reduced ejection fraction patients with predominant central sleep apnoea treated with adaptive servo ventilation. Bias is inevitable and needs to be taken into account in any kind of on-treatment analysis in positive airway pressure studies. Copyright ©ERS 2017.

Specifying the ovarian cancer risk threshold of 'premenopausal risk-reducing salpingo-oophorectomy' for ovarian cancer prevention: a cost-effectiveness analysis.

PubMed

Manchanda, Ranjit; Legood, Rosa; Antoniou, Antonis C; Gordeev, Vladimir S; Menon, Usha

2016-09-01

Risk-reducing salpingo-oophorectomy (RRSO) is the most effective intervention to prevent ovarian cancer (OC). It is only available to high-risk women with >10% lifetime OC risk. This threshold has not been formally tested for cost-effectiveness. To specify the OC risk thresholds for RRSO being cost-effective for preventing OC in premenopausal women. The costs as well as effects of surgical prevention ('RRSO') were compared over a lifetime with 'no RRSO' using a decision analysis model. RRSO was undertaken in premenopausal women >40 years. The model was evaluated at lifetime OC risk levels: 2%, 4%, 5%, 6%, 8% and 10%. Costs and outcomes are discounted at 3.5%. Uncertainty in the model was assessed using both deterministic sensitivity analysis and probabilistic sensitivity analysis (PSA). Outcomes included in the analyses were OC, breast cancer (BC) and additional deaths from coronary heart disease. Total costs and effects were estimated in terms of quality-adjusted life-years (QALYs); incidence of OC and BC; as well as incremental cost-effectiveness ratio (ICER). Published literature, Nurses Health Study, British National Formulary, Cancer Research UK, National Institute for Health and Care Excellence guidelines and National Health Service reference costs. The time horizon is lifetime and perspective: payer. Premenopausal RRSO is cost-effective at 4% OC risk (life expectancy gained=42.7 days, ICER=£19 536/QALY) with benefits largely driven by reduction in BC risk. RRSO remains cost-effective at >8.2% OC risk without hormone replacement therapy (ICER=£29 071/QALY, life expectancy gained=21.8 days) or 6%if BC risk reduction=0 (ICER=£27 212/QALY, life expectancy gained=35.3 days). Sensitivity analysis indicated results are not impacted much by costs of surgical prevention or treatment of OC/ BC or cardiovascular disease. However, results were sensitive to RRSO utility scores. Additionally, 37%, 61%, 74%, 84%, 96% and 99.5% simulations on PSA are cost-effective for RRSO at the 2%, 4%, 5%, 6%, 8% and 10% levels of OC risk, respectively. Premenopausal RRSO appears to be extremely cost-effective at ≥4% lifetime OC risk, with ≥42.7 days gain in life expectancy if compliance with hormone replacement therapy is high. Current guidelines should be re-evaluated to reduce the RRSO OC risk threshold to benefit a number of at-risk women who presently cannot access risk-reducing surgery. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Security Investment in Contagious Networks.

PubMed

Hasheminasab, Seyed Alireza; Tork Ladani, Behrouz

2018-01-16

Security of the systems is normally interdependent in such a way that security risks of one part affect other parts and threats spread through the vulnerable links in the network. So, the risks of the systems can be mitigated through investments in the security of interconnecting links. This article takes an innovative look at the problem of security investment of nodes on their vulnerable links in a given contagious network as a game-theoretic model that can be applied to a variety of applications including information systems. In the proposed game model, each node computes its corresponding risk based on the value of its assets, vulnerabilities, and threats to determine the optimum level of security investments on its external links respecting its limited budget. Furthermore, direct and indirect nonlinear influences of a node's security investment on the risks of other nodes are considered. The existence and uniqueness of the game's Nash equilibrium in the proposed game are also proved. Further analysis of the model in a practical case revealed that taking advantage of the investment effects of other players, perfectly rational players (i.e., those who use the utility function of the proposed game model) make more cost-effective decisions than selfish nonrational or semirational players. © 2018 Society for Risk Analysis.

Exploring the Specifications of Spatial Adjacencies and Weights in Bayesian Spatial Modeling with Intrinsic Conditional Autoregressive Priors in a Small-area Study of Fall Injuries

PubMed Central

Law, Jane

2016-01-01

Intrinsic conditional autoregressive modeling in a Bayeisan hierarchical framework has been increasingly applied in small-area ecological studies. This study explores the specifications of spatial structure in this Bayesian framework in two aspects: adjacency, i.e., the set of neighbor(s) for each area; and (spatial) weight for each pair of neighbors. Our analysis was based on a small-area study of falling injuries among people age 65 and older in Ontario, Canada, that was aimed to estimate risks and identify risk factors of such falls. In the case study, we observed incorrect adjacencies information caused by deficiencies in the digital map itself. Further, when equal weights was replaced by weights based on a variable of expected count, the range of estimated risks increased, the number of areas with probability of estimated risk greater than one at different probability thresholds increased, and model fit improved. More importantly, significance of a risk factor diminished. Further research to thoroughly investigate different methods of variable weights; quantify the influence of specifications of spatial weights; and develop strategies for better defining spatial structure of a map in small-area analysis in Bayesian hierarchical spatial modeling is recommended. PMID:29546147

Analysis of Traffic Crashes Involving Pedestrians Using Big Data: Investigation of Contributing Factors and Identification of Hotspots.

PubMed

Xie, Kun; Ozbay, Kaan; Kurkcu, Abdullah; Yang, Hong

2017-08-01

This study aims to explore the potential of using big data in advancing the pedestrian risk analysis including the investigation of contributing factors and the hotspot identification. Massive amounts of data of Manhattan from a variety of sources were collected, integrated, and processed, including taxi trips, subway turnstile counts, traffic volumes, road network, land use, sociodemographic, and social media data. The whole study area was uniformly split into grid cells as the basic geographical units of analysis. The cell-structured framework makes it easy to incorporate rich and diversified data into risk analysis. The cost of each crash, weighted by injury severity, was assigned to the cells based on the relative distance to the crash site using a kernel density function. A tobit model was developed to relate grid-cell-specific contributing factors to crash costs that are left-censored at zero. The potential for safety improvement (PSI) that could be obtained by using the actual crash cost minus the cost of "similar" sites estimated by the tobit model was used as a measure to identify and rank pedestrian crash hotspots. The proposed hotspot identification method takes into account two important factors that are generally ignored, i.e., injury severity and effects of exposure indicators. Big data, on the one hand, enable more precise estimation of the effects of risk factors by providing richer data for modeling, and on the other hand, enable large-scale hotspot identification with higher resolution than conventional methods based on census tracts or traffic analysis zones. © 2017 Society for Risk Analysis.

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

PubMed

Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

2015-06-01

To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

A prognostic index for natural killer cell lymphoma after non-anthracycline-based treatment: a multicentre, retrospective analysis.

PubMed

Kim, Seok Jin; Yoon, Dok Hyun; Jaccard, Arnaud; Chng, Wee Joo; Lim, Soon Thye; Hong, Huangming; Park, Yong; Chang, Kian Meng; Maeda, Yoshinobu; Ishida, Fumihiro; Shin, Dong-Yeop; Kim, Jin Seok; Jeong, Seong Hyun; Yang, Deok-Hwan; Jo, Jae-Cheol; Lee, Gyeong-Won; Choi, Chul Won; Lee, Won-Sik; Chen, Tsai-Yun; Kim, Kiyeun; Jung, Sin-Ho; Murayama, Tohru; Oki, Yasuhiro; Advani, Ranjana; d'Amore, Francesco; Schmitz, Norbert; Suh, Cheolwon; Suzuki, Ritsuro; Kwong, Yok Lam; Lin, Tong-Yu; Kim, Won Seog

2016-03-01

The clinical outcome of extranodal natural killer T-cell lymphoma (ENKTL) has improved substantially as a result of new treatment strategies with non-anthracycline-based chemotherapies and upfront use of concurrent chemoradiotherapy or radiotherapy. A new prognostic model based on the outcomes obtained with these contemporary treatments was warranted. We did a retrospective study of patients with newly diagnosed ENKTL without any previous treatment history for the disease who were given non-anthracycline-based chemotherapies with or without upfront concurrent chemoradiotherapy or radiotherapy with curative intent. A prognostic model to predict overall survival and progression-free survival on the basis of pretreatment clinical and laboratory characteristics was developed by filling a multivariable model on the basis of the dataset with complete data for the selected risk factors for an unbiased prediction model. The final model was applied to the patients who had complete data for the selected risk factors. We did a validation analysis of the prognostic model in an independent cohort. We did multivariate analyses of 527 patients who were included from 38 hospitals in 11 countries in the training cohort. Analyses showed that age greater than 60 years, stage III or IV disease, distant lymph-node involvement, and non-nasal type disease were significantly associated with overall survival and progression-free survival. We used these data as the basis for the prognostic index of natural killer lymphoma (PINK), in which patients are stratified into low-risk (no risk factors), intermediate-risk (one risk factor), or high-risk (two or more risk factors) groups, which were associated with 3-year overall survival of 81% (95% CI 75-86), 62% (55-70), and 25% (20-34), respectively. In the 328 patients with data for Epstein-Barr virus DNA, a detectable viral DNA titre was an independent prognostic factor for overall survival. When these data were added to PINK as the basis for another prognostic index (PINK-E)-which had similar low-risk (zero or one risk factor), intermediate-risk (two risk factors), and high-risk (three or more risk factors) categories-significant associations with overall survival were noted (81% [95% CI 75-87%], 55% (44-66), and 28% (18-40%), respectively). These results were validated and confirmed in an independent cohort, although the PINK-E model was only significantly associated with the high-risk group compared with the low-risk group. PINK and PINK-E are new prognostic models that can be used to develop risk-adapted treatment approaches for patients with ENKTL being treated in the contemporary era of non-anthracycline-based therapy. Samsung Biomedical Research Institute. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of the CO2-PENS risk analysis tool to the Rock Springs Uplift, Wyoming

USGS Publications Warehouse

Stauffer, P.H.; Pawar, R.J.; Surdam, R.C.; Jiao, Z.; Deng, H.; Lettelier, B.C.; Viswanathan, H.S.; Sanzo, D.L.; Keating, G.N.

2011-01-01

We describe preliminary application of the CO2-PENS performance and risk analysis tool to a planned geologic CO2 sequestration demonstration project in the Rock Springs Uplift (RSU), located in south western Wyoming. We use data from the RSU to populate CO2-PENS, an evolving system-level modeling tool developed at Los Alamos National Laboratory. This tool has been designed to generate performance and risk assessment calculations for the geologic sequestration of carbon dioxide. Our approach follows Systems Analysis logic and includes estimates of uncertainty in model parameters and Monte-Carlo simulations that lead to probabilistic results. Probabilistic results provide decision makers with a range in the likelihood of different outcomes. Herein we present results from a newly implemented approach in CO 2-PENS that captures site-specific spatially coherent details such as topography on the reservoir/cap-rock interface, changes in saturation and pressure during injection, and dip on overlying aquifers that may be impacted by leakage upward through wellbores and faults. We present simulations of CO 2 injection under different uncertainty distributions for hypothetical leaking wells and faults. Although results are preliminary and to be used only for demonstration of the approach, future results of the risk analysis will form the basis for a discussion on methods to reduce uncertainty in the risk calculations. Additionally, we present ideas on using the model to help locate monitoring equipment to detect potential leaks. By maintaining site-specific details in the CO2-PENS analysis we provide a tool that allows more logical presentations to stakeholders in the region. ?? 2011 Published by Elsevier Ltd.

Association between C677T and A1298C polymorphisms of the MTHFR gene and risk of male infertility: a meta-analysis.

PubMed

Yang, Y; Luo, Y Y; Wu, S; Tang, Y D; Rao, X D; Xiong, L; Tan, M; Deng, M Z; Liu, H

2016-04-26

Published studies on the association between the C677T and A1298C polymorphisms of the methylenetetrahydrofolate reductase (MTHFR) gene and male infertility risk are controversial. To obtain a more precise evaluation, we performed a meta-analysis based on published case-control studies. We conducted an electronic search of PubMed, EMBASE, the Cochrane Library, the Web of Science, and the China Knowledge Resource Integrated Database for papers on MTHFR gene C677T and A1298C polymorphisms and male infertility risk. Pooled odds ratios (ORs) with 95% confidence intervals (95%CIs) were used to assess the strength of association in homozygote, heterozygote, dominant, recessive, and additive models. Statistical heterogeneity, test of publication bias, and sensitivity analysis were carried out using the STATA software (Version 13.0). Overall, 21 studies of C677T (4505 cases and 4024 controls) and 13 studies of A1298C (2785 cases and 3094 controls) were included in this meta-analysis. For C677T, the homozygote comparison results were OR = 1.629, 95%CI (1.215- 2.184), and the recessive model results were OR = 1.462 (1.155- 1.850). For A1298C, the homozygote comparison results were OR = 1.289 (1.029-1.616), and the recessive model results were OR = 1.288 (1.034-1.604). In conclusion, the current meta-analysis showed that the MTHFR C677T polymorphism was associated with a significantly increased male infertility risk in the Asian and overall populations, but not in the Caucasian population, and there was a significant association between the A1298C polymorphism and male infertility risk in the Asian, Caucasian, and overall groups.

A Multimethodological Analysis of Cumulative Risk and Allostatic Load among Rural Children.

ERIC Educational Resources Information Center

Evans, Gary W.

2003-01-01

This study modeled physical and psychosocial aspects of home environment and personal characteristics in a cumulative risk heuristic. Found that elevated cumulative risk was associated with heightened cardiovascular and neuroendocrine parameters, increased deposition of body fat, and higher summary index of total allostatic load. Replicated…

42 CFR 81.11 - Use of uncertainty analysis in NIOSH-IREP.

Code of Federal Regulations, 2010 CFR

2010-10-01

... uncertainties in estimating: radiation dose incurred by the covered employee; the radiation dose-cancer relationship (statistical uncertainty in the specific cancer risk model); the extrapolation of risk (risk transfer) from the Japanese to the U.S. population; differences in the amount of cancer effect caused by...

42 CFR 81.11 - Use of uncertainty analysis in NIOSH-IREP.

Code of Federal Regulations, 2011 CFR

2011-10-01

... uncertainties in estimating: radiation dose incurred by the covered employee; the radiation dose-cancer relationship (statistical uncertainty in the specific cancer risk model); the extrapolation of risk (risk transfer) from the Japanese to the U.S. population; differences in the amount of cancer effect caused by...

42 CFR 81.11 - Use of uncertainty analysis in NIOSH-IREP.

Code of Federal Regulations, 2012 CFR

2012-10-01

... uncertainties in estimating: radiation dose incurred by the covered employee; the radiation dose-cancer relationship (statistical uncertainty in the specific cancer risk model); the extrapolation of risk (risk transfer) from the Japanese to the U.S. population; differences in the amount of cancer effect caused by...

42 CFR 81.11 - Use of uncertainty analysis in NIOSH-IREP.

Code of Federal Regulations, 2013 CFR

2013-10-01

... uncertainties in estimating: radiation dose incurred by the covered employee; the radiation dose-cancer relationship (statistical uncertainty in the specific cancer risk model); the extrapolation of risk (risk transfer) from the Japanese to the U.S. population; differences in the amount of cancer effect caused by...

42 CFR 81.11 - Use of uncertainty analysis in NIOSH-IREP.

Code of Federal Regulations, 2014 CFR

2014-10-01

... uncertainties in estimating: radiation dose incurred by the covered employee; the radiation dose-cancer relationship (statistical uncertainty in the specific cancer risk model); the extrapolation of risk (risk transfer) from the Japanese to the U.S. population; differences in the amount of cancer effect caused by...

Distribution of uncertainties at the municipality level for flood risk modelling along the river Meuse: implications for policy-making

NASA Astrophysics Data System (ADS)

Pirotton, Michel; Stilmant, Frédéric; Erpicum, Sébastien; Dewals, Benjamin; Archambeau, Pierre

2016-04-01

Flood risk modelling has been conducted for the whole course of the river Meuse in Belgium. Major cities, such as Liege (200,000 inh.) and Namur (110,000 inh.), are located in the floodplains of river Meuse. Particular attention has been paid to uncertainty analysis and its implications for decision-making. The modelling chain contains flood frequency analysis, detailed 2D hydraulic computations, damage modelling and risk calculation. The relative importance of each source of uncertainty to the overall results uncertainty has been estimated by considering several alternate options for each step of the analysis: different distributions were considered in the flood frequency analysis; the influence of modelling assumptions and boundary conditions (e.g., steady vs. unsteady) were taken into account for the hydraulic computation; two different landuse classifications and two sets of damage functions were used; the number of exceedance probabilities involved in the risk calculation (by integration of the risk-curves) was varied. In addition, the sensitivity of the results with respect to increases in flood discharges was assessed. The considered increases are consistent with a "wet" climate change scenario for the time horizons 2021-2050 and 2071-2100 (Detrembleur et al., 2015). The results of hazard computation differ significantly between the upper and lower parts of the course of river Meuse in Belgium. In the former, inundation extents grow gradually as the considered flood discharge is increased (i.e. the exceedance probability is reduced), while in the downstream part, protection structures (mainly concrete walls) prevent inundation for flood discharges corresponding to exceedance probabilities of 0.01 and above (in the present climate). For higher discharges, large inundation extents are obtained in the floodplains. The highest values of risk (mean annual damage) are obtained in the municipalities which undergo relatively frequent flooding (upper part of the river), as well as in those of the downstream part of the Meuse in which flow depths in the urbanized floodplains are particularly high when inundation occurs. This is the case of the city of Liege, as a result of a subsidence process following former mining activities. For a given climate scenario, the uncertainty ranges affecting flood risk estimates are significant; but not so much that the results for the different municipalities would overlap substantially. Therefore, these uncertainties do not hamper prioritization in terms of allocation of risk reduction measures at the municipality level. In the present climate, the uncertainties arising from flood frequency analysis have a negligible influence in the upper part of the river, while they have a considerable impact on risk modelling in the lower part, where a threshold effect was observed due to the flood protection structures (sudden transition from no inundation to massive flooding when a threshold discharge is exceeded). Varying the number of exceedance probabilities in the integration of the risk curve has different effects for different municipalities; but it does not change the ranking of the municipalities in terms of flood risk. For the other scenarios, damage estimation contributes most to the overall uncertainties. As shown by this study, the magnitude of the uncertainty and its main origin vary in space and in time. This emphasizes the paramount importance of conducting distributed uncertainty analyses. In the considered study area, prioritization of risk reduction means can be reliably performed despite the modelling uncertainties. Reference Detrembleur, S., Stilmant, F., Dewals, B., Erpicum, S., Archambeau, P., & Pirotton, M. (2015). Impacts of climate change on future flood damage on the river Meuse, with a distributed uncertainty analysis. Natural Hazards, 77(3), 1533-1549. Acknowledgement Part of this research was funded through the ARC grant for Concerted Research Actions, financed by the Wallonia-Brussels Federation. It was also supported by the NWE Interreg IVB Program.

Analysis of difference of association between polymorphisms in the XRCC5, RPA3 and RTEL1 genes and glioma, astrocytoma and glioblastoma

PubMed Central

Jin, Tianbo; Wang, Yuan; Li, Gang; Du, Shuli; Yang, Hua; Geng, Tingting; Hou, Peng; Gong, Yongkuan

2015-01-01

Background: Gliomas are the most common aggressive brain tumors and have many complex pathological types. Previous reports have discovered that genetic mutations are associated with the risk of glioma. However, it is unclear whether uniform genetic mutations exist difference between glioma and its two pathological types in the Han Chinese population. Materials and methods: We evaluated 20 SNPs of 703 glioma cases (338 astrocytoma cases, 122 glioblastoma cases) and 635 controls in a Han Chinese population using χ2 test and genetic model analysis. Results: In three case-control studies, we found rs9288516 in XRCC5 gene showed a decreased risk of glioma (OR, 0.85; 95% CI, 0.73-0.99; P = 0.042) and glioblastoma (OR, 0.70; 95% CI, 0.52-0.92; P = 0.001) in the allele model. We identified rs414805 in RPA3 gene showed an increased risk of glioblastoma in allele model (OR, 1.38; 95% CI, 1.00-1.89; P = 0.047) and dominant model (OR, 1.57; 95% CI, 1.05-2.35; P = 0.027), analysis respectively. Meanwhile, rs2297440 in RTEL1 gene showed an increased risk of glioma (OR, 1.30; 95% CI, 1.10-1.54; P = 0.002) and astrocytoma (OR, 1.26; 95% CI, 1.02-1.54; P = 0.029) in the allele model. In addition, we also observed a haplotype of “GCT” in the RTEL1 gene with an increased risk of astrocytoma (P = 0.005). Conclusions: Polymorphisms in the XRCC5, RPA3 and RTEL1 genes, combinating with previous reaserches, are associated with glioma developing. However, those genes mutations may play different roles in the glioma, astrocytoma and glioblastoma, respectively. PMID:26328260

Analysis of difference of association between polymorphisms in the XRCC5, RPA3 and RTEL1 genes and glioma, astrocytoma and glioblastoma.

PubMed

Jin, Tianbo; Wang, Yuan; Li, Gang; Du, Shuli; Yang, Hua; Geng, Tingting; Hou, Peng; Gong, Yongkuan

2015-01-01

Gliomas are the most common aggressive brain tumors and have many complex pathological types. Previous reports have discovered that genetic mutations are associated with the risk of glioma. However, it is unclear whether uniform genetic mutations exist difference between glioma and its two pathological types in the Han Chinese population. We evaluated 20 SNPs of 703 glioma cases (338 astrocytoma cases, 122 glioblastoma cases) and 635 controls in a Han Chinese population using χ(2) test and genetic model analysis. In three case-control studies, we found rs9288516 in XRCC5 gene showed a decreased risk of glioma (OR, 0.85; 95% CI, 0.73-0.99; P = 0.042) and glioblastoma (OR, 0.70; 95% CI, 0.52-0.92; P = 0.001) in the allele model. We identified rs414805 in RPA3 gene showed an increased risk of glioblastoma in allele model (OR, 1.38; 95% CI, 1.00-1.89; P = 0.047) and dominant model (OR, 1.57; 95% CI, 1.05-2.35; P = 0.027), analysis respectively. Meanwhile, rs2297440 in RTEL1 gene showed an increased risk of glioma (OR, 1.30; 95% CI, 1.10-1.54; P = 0.002) and astrocytoma (OR, 1.26; 95% CI, 1.02-1.54; P = 0.029) in the allele model. In addition, we also observed a haplotype of "GCT" in the RTEL1 gene with an increased risk of astrocytoma (P = 0.005). Polymorphisms in the XRCC5, RPA3 and RTEL1 genes, combinating with previous reaserches, are associated with glioma developing. However, those genes mutations may play different roles in the glioma, astrocytoma and glioblastoma, respectively.

Quantifying Risks and Uncertainties Associated with Induced Seismicity due to CO2 Injection into Geologic Formations with Faults

NASA Astrophysics Data System (ADS)

Hou, Z.; Nguyen, B. N.; Bacon, D. H.; White, M. D.; Murray, C. J.

2016-12-01

A multiphase flow and reactive transport simulator named STOMP-CO2-R has been developed and coupled to the ABAQUS® finite element package for geomechanical analysis enabling comprehensive thermo-hydro-geochemical-mechanical (THMC) analyses. The coupled THMC simulator has been applied to analyze faulted CO2 reservoir responses (e.g., stress and strain distributions, pressure buildup, slip tendency factor, pressure margin to fracture) with various complexities in fault and reservoir structures and mineralogy. Depending on the geological and reaction network settings, long-term injection of CO2 can have a significant effect on the elastic stiffness and permeability of formation rocks. In parallel, an uncertainty quantification framework (UQ-CO2), which consists of entropy-based prior uncertainty representation, efficient sampling, geostatistical reservoir modeling, and effective response surface analysis, has been developed for quantifying risks and uncertainties associated with CO2 sequestration. It has been demonstrated for evaluating risks in CO2 leakage through natural pathways and wellbores, and for developing predictive reduced order models. Recently, a parallel STOMP-CO2-R has been developed and the updated STOMP/ABAQUS model has been proven to have a great scalability, which makes it possible to integrate the model with the UQ framework to effectively and efficiently explore multidimensional parameter space (e.g., permeability, elastic modulus, crack orientation, fault friction coefficient) for a more systematic analysis of induced seismicity risks.

Recurrence risk model for esophageal cancer after radical surgery.

PubMed

Lu, Jincheng; Tao, Hua; Song, Dan; Chen, Cheng

2013-10-01

The aim of the present study was to construct a risk assessment model which was tested by disease-free survival (DFS) of esophageal cancer after radical surgery. A total of 164 consecutive esophageal cancer patients who had undergone radical surgery between January 2005 and December 2006 were retrospectively analyzed. The cutpoint of value at risk (VaR) was inferred by stem-and-leaf plot, as well as by independent-samples t-test for recurrence-free time, further confirmed by crosstab chi-square test, univariate analysis and Cox regression analysis for DFS. The cutpoint of VaR was 0.3 on the basis of our model. The rate of recurrence was 30.3% (30/99) and 52.3% (34/65) in VaR <0.3 and VaR ≥0.3 (chi-square test, (χ) (2) =7.984, P=0.005), respectively. The 1-, 3-, and 5-year DFS of esophageal cancer after radical surgery was 70.4%, 48.7%, and 45.3%, respectively in VaR ≥0.3, whereas 91.5%, 75.8%, and 67.3%, respectively in VaR <0.3 (Log-rank test, (χ) (2) =9.59, P=0.0020), and further confirmed by Cox regression analysis [hazard ratio =2.10, 95% confidence interval (CI): 1.2649-3.4751; P=0.0041]. The model could be applied for integrated assessment of recurrence risk after radical surgery for esophageal cancer.

Recurrence risk model for esophageal cancer after radical surgery

PubMed Central

Tao, Hua; Song, Dan; Chen, Cheng

2013-01-01

Objective The aim of the present study was to construct a risk assessment model which was tested by disease-free survival (DFS) of esophageal cancer after radical surgery. Methods A total of 164 consecutive esophageal cancer patients who had undergone radical surgery between January 2005 and December 2006 were retrospectively analyzed. The cutpoint of value at risk (VaR) was inferred by stem-and-leaf plot, as well as by independent-samples t-test for recurrence-free time, further confirmed by crosstab chi-square test, univariate analysis and Cox regression analysis for DFS. Results The cutpoint of VaR was 0.3 on the basis of our model. The rate of recurrence was 30.3% (30/99) and 52.3% (34/65) in VaR <0.3 and VaR ≥0.3 (chi-square test, χ2 =7.984, P=0.005), respectively. The 1-, 3-, and 5-year DFS of esophageal cancer after radical surgery was 70.4%, 48.7%, and 45.3%, respectively in VaR ≥0.3, whereas 91.5%, 75.8%, and 67.3%, respectively in VaR <0.3 (Log-rank test, χ2 =9.59, P=0.0020), and further confirmed by Cox regression analysis [hazard ratio =2.10, 95% confidence interval (CI): 1.2649-3.4751; P=0.0041]. Conclusions The model could be applied for integrated assessment of recurrence risk after radical surgery for esophageal cancer. PMID:24255579

Cyber Risk Management for Critical Infrastructure: A Risk Analysis Model and Three Case Studies.

PubMed

Paté-Cornell, M-Elisabeth; Kuypers, Marshall; Smith, Matthew; Keller, Philip

2018-02-01

Managing cyber security in an organization involves allocating the protection budget across a spectrum of possible options. This requires assessing the benefits and the costs of these options. The risk analyses presented here are statistical when relevant data are available, and system-based for high-consequence events that have not happened yet. This article presents, first, a general probabilistic risk analysis framework for cyber security in an organization to be specified. It then describes three examples of forward-looking analyses motivated by recent cyber attacks. The first one is the statistical analysis of an actual database, extended at the upper end of the loss distribution by a Bayesian analysis of possible, high-consequence attack scenarios that may happen in the future. The second is a systems analysis of cyber risks for a smart, connected electric grid, showing that there is an optimal level of connectivity. The third is an analysis of sequential decisions to upgrade the software of an existing cyber security system or to adopt a new one to stay ahead of adversaries trying to find their way in. The results are distributions of losses to cyber attacks, with and without some considered countermeasures in support of risk management decisions based both on past data and anticipated incidents. © 2017 Society for Risk Analysis.

The returns and risks of investment portfolio in a financial market

NASA Astrophysics Data System (ADS)

Li, Jiang-Cheng; Mei, Dong-Cheng

2014-07-01

The returns and risks of investment portfolio in a financial system was investigated by constructing a theoretical model based on the Heston model. After the theoretical model and analysis of portfolio were calculated and analyzed, we find the following: (i) The statistical properties (i.e., the probability distribution, the variance and loss rate of equity portfolio return) between simulation results of the theoretical model and the real financial data obtained from Dow Jones Industrial Average are in good agreement; (ii) The maximum dispersion of the investment portfolio is associated with the maximum stability of the equity portfolio return and minimal investment risks; (iii) An increase of the investment period and a worst investment period are associated with a decrease of stability of the equity portfolio return and a maximum investment risk, respectively.

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

Probabilistic modeling of percutaneous absorption for risk-based exposure assessments and transdermal drug delivery.

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

Ho, Clifford Kuofei

Chemical transport through human skin can play a significant role in human exposure to toxic chemicals in the workplace, as well as to chemical/biological warfare agents in the battlefield. The viability of transdermal drug delivery also relies on chemical transport processes through the skin. Models of percutaneous absorption are needed for risk-based exposure assessments and drug-delivery analyses, but previous mechanistic models have been largely deterministic. A probabilistic, transient, three-phase model of percutaneous absorption of chemicals has been developed to assess the relative importance of uncertain parameters and processes that may be important to risk-based assessments. Penetration routes through the skinmore » that were modeled include the following: (1) intercellular diffusion through the multiphase stratum corneum; (2) aqueous-phase diffusion through sweat ducts; and (3) oil-phase diffusion through hair follicles. Uncertainty distributions were developed for the model parameters, and a Monte Carlo analysis was performed to simulate probability distributions of mass fluxes through each of the routes. Sensitivity analyses using stepwise linear regression were also performed to identify model parameters that were most important to the simulated mass fluxes at different times. This probabilistic analysis of percutaneous absorption (PAPA) method has been developed to improve risk-based exposure assessments and transdermal drug-delivery analyses, where parameters and processes can be highly uncertain.« less

Underground Test Area Subproject Phase I Data Analysis Task. Volume VII - Tritium Transport Model Documentation Package

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

None

Volume VII of the documentation for the Phase I Data Analysis Task performed in support of the current Regional Flow Model, Transport Model, and Risk Assessment for the Nevada Test Site Underground Test Area Subproject contains the tritium transport model documentation. Because of the size and complexity of the model area, a considerable quantity of data was collected and analyzed in support of the modeling efforts. The data analysis task was consequently broken into eight subtasks, and descriptions of each subtask's activities are contained in one of the eight volumes that comprise the Phase I Data Analysis Documentation.

Disease mapping based on stochastic SIR-SI model for Dengue and Chikungunya in Malaysia

NASA Astrophysics Data System (ADS)

Samat, N. A.; Ma'arof, S. H. Mohd Imam

2014-12-01

This paper describes and demonstrates a method for relative risk estimation which is based on the stochastic SIR-SI vector-borne infectious disease transmission model specifically for Dengue and Chikungunya diseases in Malaysia. Firstly, the common compartmental model for vector-borne infectious disease transmission called the SIR-SI model (susceptible-infective-recovered for human populations; susceptible-infective for vector populations) is presented. This is followed by the explanations on the stochastic SIR-SI model which involve the Bayesian description. This stochastic model then is used in the relative risk formulation in order to obtain the posterior relative risk estimation. Then, this relative estimation model is demonstrated using Dengue and Chikungunya data of Malaysia. The viruses of these diseases are transmitted by the same type of female vector mosquito named Aedes Aegypti and Aedes Albopictus. Finally, the findings of the analysis of relative risk estimation for both Dengue and Chikungunya diseases are presented, compared and displayed in graphs and maps. The distribution from risk maps show the high and low risk area of Dengue and Chikungunya diseases occurrence. This map can be used as a tool for the prevention and control strategies for both diseases.

Disease mapping based on stochastic SIR-SI model for Dengue and Chikungunya in Malaysia

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

Samat, N. A.; Ma'arof, S. H. Mohd Imam

This paper describes and demonstrates a method for relative risk estimation which is based on the stochastic SIR-SI vector-borne infectious disease transmission model specifically for Dengue and Chikungunya diseases in Malaysia. Firstly, the common compartmental model for vector-borne infectious disease transmission called the SIR-SI model (susceptible-infective-recovered for human populations; susceptible-infective for vector populations) is presented. This is followed by the explanations on the stochastic SIR-SI model which involve the Bayesian description. This stochastic model then is used in the relative risk formulation in order to obtain the posterior relative risk estimation. Then, this relative estimation model is demonstrated using Denguemore » and Chikungunya data of Malaysia. The viruses of these diseases are transmitted by the same type of female vector mosquito named Aedes Aegypti and Aedes Albopictus. Finally, the findings of the analysis of relative risk estimation for both Dengue and Chikungunya diseases are presented, compared and displayed in graphs and maps. The distribution from risk maps show the high and low risk area of Dengue and Chikungunya diseases occurrence. This map can be used as a tool for the prevention and control strategies for both diseases.« less

Financial and risk considerations for successful disease management programs.

PubMed

Baldwin, A L

1999-11-01

Results for disease management [DM] programs have not been as positive as hoped because of clinical issues, lack of access to capital, and administrative issues. The financial experience of DM programs can be quite volatile. Financial projections that are protocol-based, rather than experience-based, may understate the revenue required and the range of possible costs for a DM program by understating the impact of complicating conditions and comorbidities. Actuarial tools (risk analysis and risk projection models) support better understanding of DM contracts. In particular, these models can provide the ability to quantify the impact of the factors that drive costs of a contract and the volatility of those costs. This analysis can assist DM companies in setting appropriate revenue and capital targets. Similar analysis by health plans can identify diseases that are good candidates for DM programs and can provide the basis for performance targets.

Geographic analysis of shigellosis in Vietnam.

PubMed

Kim, Deok Ryun; Ali, Mohammad; Thiem, Vu Dinh; Park, Jin-Kyung; von Seidlein, Lorenz; Clemens, John

2008-12-01

Geographic and ecological analysis may provide investigators useful ecological information for the control of shigellosis. This paper provides distribution of individual Shigella species in space, and ecological covariates for shigellosis in Nha Trang, Vietnam. Data on shigellosis in neighborhoods were used to identify ecological covariates. A Bayesian hierarchical model was used to obtain joint posterior distribution of model parameters and to construct smoothed risk maps for shigellosis. Neighborhoods with a high proportion of worshippers of traditional religion, close proximity to hospital, or close proximity to the river had increased risk for shigellosis. The ecological covariates associated with Shigella flexneri differed from the covariates for Shigella sonnei. In contrast the spatial distribution of the two species was similar. The disease maps can help identify high-risk areas of shigellosis that can be targeted for interventions. This approach may be useful for the selection of populations and the analysis of vaccine trials.

Risk Prediction Models of Locoregional Failure After Radical Cystectomy for Urothelial Carcinoma: External Validation in a Cohort of Korean Patients

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

Ku, Ja Hyeon; Kim, Myong; Jeong, Chang Wook

2014-08-01

Purpose: To evaluate the predictive accuracy and general applicability of the locoregional failure model in a different cohort of patients treated with radical cystectomy. Methods and Materials: A total of 398 patients were included in the analysis. Death and isolated distant metastasis were considered competing events, and patients without any events were censored at the time of last follow-up. The model included the 3 variables pT classification, the number of lymph nodes identified, and margin status, as follows: low risk (≤pT2), intermediate risk (≥pT3 with ≥10 nodes removed and negative margins), and high risk (≥pT3 with <10 nodes removed ormore » positive margins). Results: The bootstrap-corrected concordance index of the model 5 years after radical cystectomy was 66.2%. When the risk stratification was applied to the validation cohort, the 5-year locoregional failure estimates were 8.3%, 21.2%, and 46.3% for the low-risk, intermediate-risk, and high-risk groups, respectively. The risk of locoregional failure differed significantly between the low-risk and intermediate-risk groups (subhazard ratio [SHR], 2.63; 95% confidence interval [CI], 1.35-5.11; P<.001) and between the low-risk and high-risk groups (SHR, 4.28; 95% CI, 2.17-8.45; P<.001). Although decision curves were appropriately affected by the incidence of the competing risk, decisions about the value of the models are not likely to be affected because the model remains of value over a wide range of threshold probabilities. Conclusions: The model is not completely accurate, but it demonstrates a modest level of discrimination, adequate calibration, and meaningful net benefit gain for prediction of locoregional failure after radical cystectomy.« less

Gravity models to classify commuting vs. resident workers. An application to the analysis of residential risk in a contaminated area

PubMed Central

2011-01-01

Background The analysis of risk for the population residing and/or working in contaminated areas raises the topic of commuting. In fact, especially in contaminated areas, commuting groups are likely to be subject to lower exposure than residents. Only very recently environmental epidemiology has started considering the role of commuting as a differential source of exposure in contaminated areas. In order to improve the categorization of groups, this paper applies a gravitational model to the analysis of residential risk for workers in the Gela petrochemical complex, which began life in the early 60s in the municipality of Gela (Sicily, Italy) and is the main source of industrial pollution in the local area. Results A logistic regression model is implemented to measure the capacity of Gela "central location" to attract commuting flows from other sites. Drawing from gravity models, the proposed methodology: a) defines the probability of finding commuters from municipalities outside Gela as a function of the origin's "economic mass" and of its distance from each destination; b) establishes "commuting thresholds" relative to the origin's mass. The analysis includes 367 out of the 390 Sicilian municipalities. Results are applied to define "commuters" and "residents" within the cohort of petrochemical workers. The study population is composed of 5,627 workers. Different categories of residence in Gela are compared calculating Mortality Rate Ratios for lung cancer through a Poisson regression model, controlling for age and calendar period. The mobility model correctly classifies almost 90% of observations. Its application to the mortality analysis confirms a major risk for lung cancer associated with residence in Gela. Conclusions Commuting is a critical aspect of the health-environment relationship in contaminated areas. The proposed methodology can be replicated to different contexts when residential information is lacking or unreliable; however, a careful consideration of the territorial characteristics ("insularity" and its impact on transportation time and costs, in our case) is suggested when specifying the area of application for the mobility analysis. PMID:21272299