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

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-17

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

Integrating public risk perception into formal natural hazard risk assessment

NASA Astrophysics Data System (ADS)

Plattner, Th.; Plapp, T.; Hebel, B.

2006-06-01

An urgent need to take perception into account for risk assessment has been pointed out by relevant literature, its impact in terms of risk-related behaviour by individuals is obvious. This study represents an effort to overcome the broadly discussed question of whether risk perception is quantifiable or not by proposing a still simple but applicable methodology. A novel approach is elaborated to obtain a more accurate and comprehensive quantification of risk in comparison to present formal risk evaluation practice. A consideration of relevant factors enables a explicit quantification of individual risk perception and evaluation. The model approach integrates the effective individual risk reff and a weighted mean of relevant perception affecting factors PAF. The relevant PAF cover voluntariness of risk-taking, individual reducibility of risk, knowledge and experience, endangerment, subjective damage rating and subjective recurrence frequency perception. The approach assigns an individual weight to each PAF to represent its impact magnitude. The quantification of these weights is target-group-dependent (e.g. experts, laypersons) and may be effected by psychometric methods. The novel approach is subject to a plausibility check using data from an expert-workshop. A first model application is conducted by means of data of an empirical risk perception study in Western Germany to deduce PAF and weight quantification as well as to confirm and evaluate model applicbility and flexibility. Main fields of application will be a quantification of risk perception by individual persons in a formal and technical way e.g. for the purpose of risk communication issues in illustrating differing perspectives of experts and non-experts. For decision making processes this model will have to be applied with caution, since it is by definition not designed to quantify risk acceptance or risk evaluation. The approach may well explain how risk perception differs, but not why it differs. The formal model generates only "snap shots" and considers neither the socio-cultural nor the historical context of risk perception, since it is a highly individualistic and non-contextual approach.

The care of Filipino juvenile offenders in residential facilities evaluated using the risk-need-responsivity model.

PubMed

Spruit, Anouk; Wissink, Inge B; Stams, Geert Jan J M

2016-01-01

According to the risk-need-responsivity model of offender, assessment and rehabilitation treatment should target specific factors that are related to re-offending. This study evaluates the residential care of Filipino juvenile offenders using the risk-need-responsivity model. Risk analyses and criminogenic needs assessments (parenting style, aggression, relationships with peers, empathy, and moral reasoning) have been conducted using data of 55 juvenile offenders in four residential facilities. The psychological care has been assessed using a checklist. Statistical analyses showed that juvenile offenders had a high risk of re-offending, high aggression, difficulties in making pro-social friends, and a delayed socio-moral development. The psychological programs in the residential facilities were evaluated to be poor. The availability of the psychological care in the facilities fitted poorly with the characteristics of the juvenile offenders and did not comply with the risk-need-responsivity model. Implications for research and practice are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Research on Liquidity Risk Evaluation of Chinese A-Shares Market Based on Extension Theory

NASA Astrophysics Data System (ADS)

Bai-Qing, Sun; Peng-Xiang, Liu; Lin, Zhang; Yan-Ge, Li

This research defines the liquidity risk of stock market in matter-element theory and affair-element theory, establishes the indicator system of the forewarning for liquidity risks,designs the model and the process of early warning using the extension set method, extension dependent function and the comprehensive evaluation model. And the paper studies empirically A-shares market through the data of 1A0001, which prove that the model can better describe liquidity risk of China’s A-share market. At last, it gives the corresponding policy recommendations.

Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models.

PubMed

Campbell, William; Ganna, Andrea; Ingelsson, Erik; Janssens, A Cecile J W

2016-01-01

We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. Copyright © 2016 Elsevier Inc. All rights reserved.

Field Evaluation of an Avian Risk Assessment Model

EPA Science Inventory

We conducted two laboratory subacute dietary toxicity tests and one outdoor subacute dietary toxicity test to determine the effectiveness of the U.S. Environmental Protection Agency's deterministic risk assessment model for evaluating the potential of adverse effects to birds in ...

Mechanisms of Family Impact on African American Adolescents’ HIV-Related Behavior

PubMed Central

Kogan, Steven M.; Brody, Gene H.; Gibbons, Frederick X.; Chen, Yi-fu; Grange, Christina M.; Simons, Ronald L.; Gerrard, Meg; Cutrona, Carolyn E.

2010-01-01

A longitudinal model that tested mediating pathways between protective family processes and HIV-related behavior was evaluated with 195 African American youth. Three waves of data were collected when the youth were 13, 15, and 19 years old. Evidence of mediation and temporal priority were assessed for three constructs: academic engagement, evaluations of prototypical risk-taking peers, and affiliations with risk-promoting peers. Structural equation modeling indicated that protective family processes assessed during early adolescence were associated with HIV-related behavior during emerging adulthood and that academic engagement, evaluations of prototypical risk-taking peers, and affiliations with risk-promoting peers accounted for this association. Evidence of a specific pathway emerged: protective family processes → academic engagement negative → evaluations of prototypical risk-taking peers→ affiliations with risk-promoting peers→ HIV-related behavior. Academic engagement also was a direct predictor of HIV-related risk behavior. PMID:21643492

Research on efficiency evaluation model of integrated energy system based on hybrid multi-attribute decision-making.

PubMed

Li, Yan

2017-05-25

The efficiency evaluation model of integrated energy system, involving many influencing factors, and the attribute values are heterogeneous and non-deterministic, usually cannot give specific numerical or accurate probability distribution characteristics, making the final evaluation result deviation. According to the characteristics of the integrated energy system, a hybrid multi-attribute decision-making model is constructed. The evaluation model considers the decision maker's risk preference. In the evaluation of the efficiency of the integrated energy system, the evaluation value of some evaluation indexes is linguistic value, or the evaluation value of the evaluation experts is not consistent. These reasons lead to ambiguity in the decision information, usually in the form of uncertain linguistic values and numerical interval values. In this paper, the risk preference of decision maker is considered when constructing the evaluation model. Interval-valued multiple-attribute decision-making method and fuzzy linguistic multiple-attribute decision-making model are proposed. Finally, the mathematical model of efficiency evaluation of integrated energy system is constructed.

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.

Developing Statistical Models to Assess Transplant Outcomes Using National Registries: The Process in the United States.

PubMed

Snyder, Jon J; Salkowski, Nicholas; Kim, S Joseph; Zaun, David; Xiong, Hui; Israni, Ajay K; Kasiske, Bertram L

2016-02-01

Created by the US National Organ Transplant Act in 1984, the Scientific Registry of Transplant Recipients (SRTR) is obligated to publicly report data on transplant program and organ procurement organization performance in the United States. These reports include risk-adjusted assessments of graft and patient survival, and programs performing worse or better than expected are identified. The SRTR currently maintains 43 risk adjustment models for assessing posttransplant patient and graft survival and, in collaboration with the SRTR Technical Advisory Committee, has developed and implemented a new systematic process for model evaluation and revision. Patient cohorts for the risk adjustment models are identified, and single-organ and multiorgan transplants are defined, then each risk adjustment model is developed following a prespecified set of steps. Model performance is assessed, the model is refit to a more recent cohort before each evaluation cycle, and then it is applied to the evaluation cohort. The field of solid organ transplantation is unique in the breadth of the standardized data that are collected. These data allow for quality assessment across all transplant providers in the United States. A standardized process of risk model development using data from national registries may enhance the field.

Risk assessment and remedial policy evaluation using predictive modeling

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

Linkov, L.; Schell, W.R.

1996-06-01

As a result of nuclear industry operation and accidents, large areas of natural ecosystems have been contaminated by radionuclides and toxic metals. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions, not only economic costs but also human and environmental risk assessments are desired. This paper introduces a general framework for risk assessment and remedial policy evaluation using predictive modeling. Ecological risk assessment requires evaluation of the radionuclide distribution in ecosystems. The FORESTPATH model is used for predicting the radionuclide fate in forestmore » compartments after deposition as well as for evaluating the efficiency of remedial policies. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer annually. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus and a better methodology is required. In conclusion, FORESTPATH modeling framework could have wide applications in environmental remediation of radionuclides and toxic metals as well as in dose reconstruction and, risk-assessment.« less

Empirically evaluating decision-analytic models.

PubMed

Goldhaber-Fiebert, Jeremy D; Stout, Natasha K; Goldie, Sue J

2010-08-01

Model-based cost-effectiveness analyses support decision-making. To augment model credibility, evaluation via comparison to independent, empirical studies is recommended. We developed a structured reporting format for model evaluation and conducted a structured literature review to characterize current model evaluation recommendations and practices. As an illustration, we applied the reporting format to evaluate a microsimulation of human papillomavirus and cervical cancer. The model's outputs and uncertainty ranges were compared with multiple outcomes from a study of long-term progression from high-grade precancer (cervical intraepithelial neoplasia [CIN]) to cancer. Outcomes included 5 to 30-year cumulative cancer risk among women with and without appropriate CIN treatment. Consistency was measured by model ranges overlapping study confidence intervals. The structured reporting format included: matching baseline characteristics and follow-up, reporting model and study uncertainty, and stating metrics of consistency for model and study results. Structured searches yielded 2963 articles with 67 meeting inclusion criteria and found variation in how current model evaluations are reported. Evaluation of the cervical cancer microsimulation, reported using the proposed format, showed a modeled cumulative risk of invasive cancer for inadequately treated women of 39.6% (30.9-49.7) at 30 years, compared with the study: 37.5% (28.4-48.3). For appropriately treated women, modeled risks were 1.0% (0.7-1.3) at 30 years, study: 1.5% (0.4-3.3). To support external and projective validity, cost-effectiveness models should be iteratively evaluated as new studies become available, with reporting standardized to facilitate assessment. Such evaluations are particularly relevant for models used to conduct comparative effectiveness analyses.

Innovative neuro-fuzzy system of smart transport infrastructure for road traffic safety

NASA Astrophysics Data System (ADS)

Beinarovica, Anna; Gorobetz, Mikhail; Levchenkov, Anatoly

2017-09-01

The proposed study describes applying of neural network and fuzzy logic in transport control for safety improvement by evaluation of accidents’ risk by intelligent infrastructure devices. Risk evaluation is made by following multiple-criteria: danger, changeability and influence of changes for risk increasing. Neuro-fuzzy algorithms are described and proposed for task solution. The novelty of the proposed system is proved by deep analysis of known studies in the field. The structure of neuro-fuzzy system for risk evaluation and mathematical model is described in the paper. The simulation model of the intelligent devices for transport infrastructure is proposed to simulate different situations, assess the risks and propose the possible actions for infrastructure or vehicles to minimize the risk of possible accidents.

The HHS-HCC Risk Adjustment Model for Individual and Small Group Markets under the Affordable Care Act

PubMed Central

Kautter, John; Pope, Gregory C; Ingber, Melvin; Freeman, Sara; Patterson, Lindsey; Cohen, Michael; Keenan, Patricia

2014-01-01

Beginning in 2014, individuals and small businesses are able to purchase private health insurance through competitive Marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the second of three in this issue of the Review that describe the Department of Health and Human Services (HHS) risk adjustment methodology and focuses on the risk adjustment model. In our first companion article, we discuss the key issues and choices in developing the methodology. In this article, we present the risk adjustment model, which is named the HHS-Hierarchical Condition Categories (HHS-HCC) risk adjustment model. We first summarize the HHS-HCC diagnostic classification, which is the key element of the risk adjustment model. Then the data and methods, results, and evaluation of the risk adjustment model are presented. Fifteen separate models are developed. For each age group (adult, child, and infant), a model is developed for each cost sharing level (platinum, gold, silver, and bronze metal levels, as well as catastrophic plans). Evaluation of the risk adjustment models shows good predictive accuracy, both for individuals and for groups. Lastly, this article provides examples of how the model output is used to calculate risk scores, which are an input into the risk transfer formula. Our third companion paper describes the risk transfer formula. PMID:25360387

Social appearance anxiety, perfectionism, and fear of negative evaluation: Distinct or shared risk factors for social anxiety and eating disorders?

PubMed Central

Levinson, Cheri A.; Rodebaugh, Thomas L.; White, Emily K.; Menatti, Andrew; Weeks, Justin W.; Iacovino, Juliette M.; Warren, Cortney S.

2013-01-01

Social anxiety and eating disorders are highly comorbid. Social appearance anxiety (i.e., fear of negative evaluation of one's appearance), general fear of negative evaluation, and perfectionism have each been proposed as risk factors for both social anxiety disorder and the eating disorders. However, no research to date has examined all three factors simultaneously. Using structural equation modeling in two diverse samples (N = 236; N = 136) we tested a model in which each of these risk factors were uniquely associated with social anxiety and eating disorder symptoms. We found support for social appearance anxiety as a shared risk factor between social anxiety and eating disorder symptoms, whereas fear of negative evaluation was a risk factor only for social anxiety symptoms. Despite significant zero-order relationships, two facets of perfectionism (high standards and maladaptive perfectionism) did not emerge as a risk factor for either disorder when all constructs were considered. These results were maintained when gender, body mass index, trait negative affect, and depression were included in the model. It is possible that treating negative appearance evaluation fears may reduce both eating disorder and social anxiety symptoms. PMID:23583741

Evaluating critical uncertainty thresholds in a spatial model of forest pest invasion risk

Treesearch

Frank H. Koch; Denys Yemshanov; Daniel W. McKenney; William D. Smith

2009-01-01

Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted risk values. This study explores how increased uncertainty in a risk model’s numeric assumptions might affect the resultant risk map. We used a spatial stochastic model, integrating components for...

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

PubMed Central

2018-01-01

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

Construction risk assessment of deep foundation pit in metro station based on G-COWA method

NASA Astrophysics Data System (ADS)

You, Weibao; Wang, Jianbo; Zhang, Wei; Liu, Fangmeng; Yang, Diying

2018-05-01

In order to get an accurate understanding of the construction safety of deep foundation pit in metro station and reduce the probability and loss of risk occurrence, a risk assessment method based on G-COWA is proposed. Firstly, relying on the specific engineering examples and the construction characteristics of deep foundation pit, an evaluation index system based on the five factors of “human, management, technology, material and environment” is established. Secondly, the C-OWA operator is introduced to realize the evaluation index empowerment and weaken the negative influence of expert subjective preference. The gray cluster analysis and fuzzy comprehensive evaluation method are combined to construct the construction risk assessment model of deep foundation pit, which can effectively solve the uncertainties. Finally, the model is applied to the actual project of deep foundation pit of Qingdao Metro North Station, determine its construction risk rating is “medium”, evaluate the model is feasible and reasonable. And then corresponding control measures are put forward and useful reference are provided.

Risk Evaluation of Railway Coal Transportation Network Based on Multi Level Grey Evaluation Model

NASA Astrophysics Data System (ADS)

Niu, Wei; Wang, Xifu

2018-01-01

The railway transport mode is currently the most important way of coal transportation, and now China’s railway coal transportation network has become increasingly perfect, but there is still insufficient capacity, some lines close to saturation and other issues. In this paper, the theory and method of risk assessment, analytic hierarchy process and multi-level gray evaluation model are applied to the risk evaluation of coal railway transportation network in China. Based on the example analysis of Shanxi railway coal transportation network, to improve the internal structure and the competitiveness of the market.

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.

Evaluating Predictive Models of Software Quality

NASA Astrophysics Data System (ADS)

Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

2014-06-01

Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer

PubMed Central

Raji, Olaide Y.; Duffy, Stephen W.; Agbaje, Olorunshola F.; Baker, Stuart G.; Christiani, David C.; Cassidy, Adrian; Field, John K.

2013-01-01

Background External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit. Objective To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America. Design Case–control and prospective cohort study. Setting Europe and North America. Patients Participants in the European Early Lung Cancer (EUELC) and Harvard case–control studies and the LLP population-based prospective cohort (LLPC) study. Measurements 5-year absolute risks for lung cancer predicted by the LLP model. Results The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening. Limitations The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model. Conclusion Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population risk thresholds for initiating lung cancer screening. Primary Funding Source Roy Castle Lung Cancer Foundation. PMID:22910935

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Modeling Research Project Risks with Fuzzy Maps

ERIC Educational Resources Information Center

Bodea, Constanta Nicoleta; Dascalu, Mariana Iuliana

2009-01-01

The authors propose a risks evaluation model for research projects. The model is based on fuzzy inference. The knowledge base for fuzzy process is built with a causal and cognitive map of risks. The map was especially developed for research projects, taken into account their typical lifecycle. The model was applied to an e-testing research…

Preclinical Torsades-de-Pointes screens: advantages and limitations of surrogate and direct approaches in evaluating proarrhythmic risk.

PubMed

Gintant, Gary A

2008-08-01

The successful development of novel drugs requires the ability to detect (and avoid) compounds that may provoke Torsades-de-Pointes (TdeP) arrhythmia while endorsing those compounds with minimal torsadogenic risk. As TdeP is a rare arrhythmia not readily observed during clinical or post-marketing studies, numerous preclinical models are employed to assess delayed or altered ventricular repolarization (surrogate markers linked to enhanced proarrhythmic risk). This review evaluates the advantages and limitations of selected preclinical models (ranging from the simplest cellular hERG current assay to the more complex in vitro perfused ventricular wedge and Langendorff heart preparations and in vivo chronic atrio-ventricular (AV)-node block model). Specific attention is paid to the utility of concentration-response relationships and "risk signatures" derived from these studies, with the intention of moving beyond predicting clinical QT prolongation and towards prediction of TdeP risk. While the more complex proarrhythmia models may be suited to addressing questionable or conflicting proarrhythmic signals obtained with simpler preclinical assays, further benchmarking of proarrhythmia models is required for their use in the robust evaluation of safety margins. In the future, these models may be able to reduce unwarranted attrition of evolving compounds while becoming pivotal in the balanced integrated risk assessment of advancing compounds.

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.

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

NASA Astrophysics Data System (ADS)

Kato, Kensuke

2016-11-01

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

Dynamic drought risk assessment using crop model and remote sensing techniques

NASA Astrophysics Data System (ADS)

Sun, H.; Su, Z.; Lv, J.; Li, L.; Wang, Y.

2017-02-01

Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

Spatially explicit risk assessment of an estuarine fish in Barataria Bay, Louisiana, following the Deepwater Horizon Oil spill: evaluating tradeoffs in model complexity and parsimony

EPA Science Inventory

As ecological risk assessments (ERA) move beyond organism-based determinations towards probabilistic population-level assessments, model complexity must be evaluated against the goals of the assessment, the information available to parameterize components with minimal dependence ...

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.

PETRORISK: a risk assessment framework for petroleum substances.

PubMed

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

2014-07-01

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

The Common Risk Model for Dams: A Portfolio Approach to Security Risk Assessments

DTIC Science & Technology

2013-06-01

and threat estimates in a way that accounts for the relationships among these variables. The CRM -D can effectively quantify the benefits of...consequence, vulnerability, and threat estimates in a way that properly accounts for the relationships among these variables. The CRM -D can effectively...Common RiskModel ( CRM ) for evaluating and comparing risks associated with the nation’s critical infrastructure. This model incorporates commonly used risk

EVALUATING QUANTITATIVE FORMULAS FOR DOSE-RESPONSE ASSESSMENT OF CHEMICAL MIXTURES

EPA Science Inventory

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

[The application of two occupation health risk assessment models in a wooden furniture manufacturing industry].

PubMed

Wang, A H; Leng, P B; Bian, G L; Li, X H; Mao, G C; Zhang, M B

2016-10-20

Objective: To explore the applicability of 2 different models of occupational health risk assessment in wooden furniture manufacturing industry. Methods: American EPA inhalation risk model and ICMM model of occupational health risk assessment were conducted to assess occupational health risk in a small wooden furniture enterprises, respectively. Results: There was poor protective measure and equipment of occupational disease in the plant. The concentration of wood dust in the air of two workshops was over occupational exposure limit (OEL) , and the C TWA was 8.9 mg/m 3 and 3.6 mg/m 3 , respectively. According to EPA model, the workers who exposed to benzene in this plant had high risk (9.7×10 -6 ~34.3×10 -6 ) of leukemia, and who exposed to formaldehyde had high risk (11.4 × 10 -6 ) of squamous cell carcinoma. There were inconsistent evaluation results using the ICMM tools of standard-based matrix and calculated risk rating. There were very high risks to be attacked by rhinocarcinoma of the workers who exposed to wood dust for the tool of calculated risk rating, while high risk for the tool of standard-based matrix. For the workers who exposed to noise, risk of noise-induced deafness was unacceptable and medium risk using two tools, respectively. Conclusion: Both EPA model and ICMM model can appropriately predict and assessthe occupational health risk in wooden furniture manufactory, ICMM due to the relatively simple operation, easy evaluation parameters, assessment of occupational - disease - inductive factors comprehensively, and more suitable for wooden furniture production enterprise.

Evaluation of risk scores for risk stratification of acute coronary syndromes in the Myocardial Infarction National Audit Project (MINAP) database.

PubMed

Gale, C P; Manda, S O M; Weston, C F; Birkhead, J S; Batin, P D; Hall, A S

2009-03-01

To compare the discriminative performance of the PURSUIT, GUSTO-1, GRACE, SRI and EMMACE risk models, assess their performance among risk supergroups and evaluate the EMMACE risk model over the wider spectrum of acute coronary syndrome (ACS). Observational study of a national registry. All acute hospitals in England and Wales. 100 686 cases of ACS between 2003 and 2005. Model performance (C-index) in predicting the likelihood of death over the time period for which they were designed. The C-index, or area under the receiver-operating curve, range 0-1, is a measure of the discriminative performance of a model. The C-indexes were: PURSUIT C-index 0.79 (95% confidence interval 0.78 to 0.80); GUSTO-1 0.80 (0.79 to 0.81); GRACE in-hospital 0.80 (0.80 to 0.81); GRACE 6-month 0.80 (0.79 to 0.80); SRI 0.79 (0.78 to 0.80); and EMMACE 0.78 (0.77 to 0.78). EMMACE maintained its ability to discriminate 30-day mortality throughout different ACS diagnoses. Recalibration of the model offered no notable improvement in performance over the original risk equation. For all models the discriminative performance was reduced in patients with diabetes, chronic renal failure or angina. The five ACS risk models maintained their discriminative performance in a large unselected English and Welsh ACS population, but performed less well in higher-risk supergroups. Simpler risk models had comparable performance to more complex risk models. The EMMACE risk score performed well across the wider spectrum of ACS diagnoses.

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.

Care zoning in a psychiatric intensive care unit: strengthening ongoing clinical risk assessment.

PubMed

Mullen, Antony; Drinkwater, Vincent; Lewin, Terry J

2014-03-01

To implement and evaluate the care zoning model in an eight-bed psychiatric intensive care unit and, specifically, to examine the model's ability to improve the documentation and communication of clinical risk assessment and management. Care zoning guides nurses in assessing clinical risk and planning care within a mental health context. Concerns about the varying quality of clinical risk assessment prompted a trial of the care zoning model in a psychiatric intensive care unit within a regional mental health facility. The care zoning model assigns patients to one of 3 'zones' according to their clinical risk, encouraging nurses to document and implement targeted interventions required to manage those risks. An implementation trial framework was used for this research to refine, implement and evaluate the impact of the model on nurses' clinical practice within the psychiatric intensive care unit, predominantly as a quality improvement initiative. The model was trialled for three months using a pre- and postimplementation staff survey, a pretrial file audit and a weekly file audit. Informal staff feedback was also sought via surveys and regular staff meetings. This trial demonstrated improvement in the quality of mental state documentation, and clinical risk information was identified more accurately. There was limited improvement in the quality of care planning and the documentation of clinical interventions. Nurses' initial concerns over the introduction of the model shifted into overall acceptance and recognition of the benefits. The results of this trial demonstrate that the care zoning model was able to improve the consistency and quality of risk assessment information documented. Care planning and evaluation of associated outcomes showed less improvement. Care zoning remains a highly applicable model for the psychiatric intensive care unit environment and is a useful tool in guiding nurses to carry out routine patient risk assessments. © 2013 John Wiley & Sons Ltd.

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.

EVALUATING RISK IN OLDER ADULTS USING PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELS

EPA Science Inventory

The rapid growth in the number of older Americans has many implications for public health, including the need to better understand the risks posed by environmental exposures to older adults. An important element for evaluating risk is the understanding of the doses of environment...

Detection of Cardiovascular Disease Risk's Level for Adults Using Naive Bayes Classifier.

PubMed

Miranda, Eka; Irwansyah, Edy; Amelga, Alowisius Y; Maribondang, Marco M; Salim, Mulyadi

2016-07-01

The number of deaths caused by cardiovascular disease and stroke is predicted to reach 23.3 million in 2030. As a contribution to support prevention of this phenomenon, this paper proposes a mining model using a naïve Bayes classifier that could detect cardiovascular disease and identify its risk level for adults. The process of designing the method began by identifying the knowledge related to the cardiovascular disease profile and the level of cardiovascular disease risk factors for adults based on the medical record, and designing a mining technique model using a naïve Bayes classifier. Evaluation of this research employed two methods: accuracy, sensitivity, and specificity calculation as well as an evaluation session with cardiologists and internists. The characteristics of cardiovascular disease are identified by its primary risk factors. Those factors are diabetes mellitus, the level of lipids in the blood, coronary artery function, and kidney function. Class labels were assigned according to the values of these factors: risk level 1, risk level 2 and risk level 3. The evaluation of the classifier performance (accuracy, sensitivity, and specificity) in this research showed that the proposed model predicted the class label of tuples correctly (above 80%). More than eighty percent of respondents (including cardiologists and internists) who participated in the evaluation session agree till strongly agreed that this research followed medical procedures and that the result can support medical analysis related to cardiovascular disease. The research showed that the proposed model achieves good performance for risk level detection of cardiovascular disease.

Empirical evaluation of the market price of risk using the CIR model

NASA Astrophysics Data System (ADS)

Bernaschi, M.; Torosantucci, L.; Uboldi, A.

2007-03-01

We describe a simple but effective method for the estimation of the market price of risk. The basic idea is to compare the results obtained by following two different approaches in the application of the Cox-Ingersoll-Ross (CIR) model. In the first case, we apply the non-linear least squares method to cross sectional data (i.e., all rates of a single day). In the second case, we consider the short rate obtained by means of the first procedure as a proxy of the real market short rate. Starting from this new proxy, we evaluate the parameters of the CIR model by means of martingale estimation techniques. The estimate of the market price of risk is provided by comparing results obtained with these two techniques, since this approach makes possible to isolate the market price of risk and evaluate, under the Local Expectations Hypothesis, the risk premium given by the market for different maturities. As a test case, we apply the method to data of the European Fixed Income Market.

A Formative Evaluation of the Children, Youth, and Families at Risk Coaching Model

ERIC Educational Resources Information Center

Olson, Jonathan R.; Smith, Burgess; Hawkey, Kyle R.; Perkins, Daniel F.; Borden, Lynne M.

2016-01-01

In this article, we describe the results of a formative evaluation of a coaching model designed to support recipients of funding through the Children, Youth, and Families at Risk (CYFAR) initiative. Results indicate that CYFAR coaches draw from a variety of types of coaching and that CYFAR principle investigators (PIs) are generally satisfied with…

Extensions of criteria for evaluating risk prediction models for public health applications.

PubMed

Pfeiffer, Ruth M

2013-04-01

We recently proposed two novel criteria to assess the usefulness of risk prediction models for public health applications. The proportion of cases followed, PCF(p), is the proportion of individuals who will develop disease who are included in the proportion p of individuals in the population at highest risk. The proportion needed to follow-up, PNF(q), is the proportion of the general population at highest risk that one needs to follow in order that a proportion q of those destined to become cases will be followed (Pfeiffer, R.M. and Gail, M.H., 2011. Two criteria for evaluating risk prediction models. Biometrics 67, 1057-1065). Here, we extend these criteria in two ways. First, we introduce two new criteria by integrating PCF and PNF over a range of values of q or p to obtain iPCF, the integrated PCF, and iPNF, the integrated PNF. A key assumption in the previous work was that the risk model is well calibrated. This assumption also underlies novel estimates of iPCF and iPNF based on observed risks in a population alone. The second extension is to propose and study estimates of PCF, PNF, iPCF, and iPNF that are consistent even if the risk models are not well calibrated. These new estimates are obtained from case-control data when the outcome prevalence in the population is known, and from cohort data, with baseline covariates and observed health outcomes. We study the efficiency of the various estimates and propose and compare tests for comparing two risk models, both of which were evaluated in the same validation data.

A risk assessment of direct and indirect exposure to emissions from a proposed hazardous waste incinerator in Puerto Rico

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

Hallinger, K.; Huggins, A.; Warner, L.

1995-12-31

An Indirect Exposure Assessment (IEA) was conducted, under USEPA`s RCRA Combustion Strategy, as part of the Part B permitting process for a proposed hazardous waste incinerator. The IEA involved identification of constituents of concern, emissions estimations, air dispersion and deposition modeling, evaluation of site-specific exposure pathways/scenarios, and food chain modeling in order to evaluate potential human health and environmental risks. The COMPDEP model was used to determine ambient ground level concentrations and dry and wet deposition rates of constituents of concern. The air modeling results were input into 50th percentile (Central) and 95th percentile (High-End) exposure scenarios which evaluated directmore » exposure via inhalation, dermal contact, and soil ingestion pathways, and indirect exposure through the food chain. The indirect pathway analysis considered the accumulation of constituents in plants and animals used as food sources by local inhabitants. Local food consumption data obtained from the Puerto Rico USDA were combined with realistic present-day and future-use exposure scenarios such as residential use, pineapple farming, and subsistence farming to obtain a comprehensive evaluation of risk, Overall risk was calculated using constituent doses and toxicity factors associated with the various routes of exposure. Risk values for each exposure pathway were summed to determine total carcinogenic and non-carcinogenic hazard to exposed individuals. A population risk assessment was also conducted in order to assess potential risks to the population surrounding the facility. Results of the assessment indicated no acute effects from constituents of concern, and a high-end excess lifetime cancer risk of approximately 6 in a million with dioxins (as 2,3,7,8-TCDD) and arsenic dominating the risk estimate.« less

[Pollution Evaluation and Risk Assessment of Heavy Metals from Atmospheric Deposition in the Parks of Nanjing].

PubMed

Wang, Cheng; Qian, Xin; Li, Hui-ming; Sun, Yi-xuan; Wang, Jin-hua

2016-05-15

Contents of heavy metals involving As, Cd, Cr, Cu, Ni, Pb and Zn from atmospheric deposition in 10 parks of Nanjing were analyzed. The pollution level, ecological risk and health risk were evaluated using Geoaccumulation Index, Potential Ecological Risk Index and the US EPA Health Risk Assessment Model, respectively. The results showed that the pollution levels of heavy metals in Swallow Rock Park, Swallow Rock Park and Mochou Lake Park were higher than the others. Compared to other cities such as Changchun, Wuhan and Beijing, the contents of heavy metals in atmospheric deposition of parks in Nanjing were higher. The evaluation results of Geoaccumulation Index showed that Pb was at moderate pollution level, Zn and Cu were between moderate and serious levels, while Cd was between serious and extreme levels. The ecological risk level of Cd was high. The assessment results of Health Risk Assessment Model indicated that there was no non-carcinogenic risk for all the seven heavy metals. For carcinogenic risk, the risks of Cd, Cr and Ni were all negligible (Risk < 1 x 10⁻⁶), whereas As had carcinogenic risk possibility but was considered to be acceptable (10⁻⁶ < Risk < 10⁻⁴).

A neural network model for credit risk evaluation.

PubMed

Khashman, Adnan

2009-08-01

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Multi Criteria Evaluation Module for RiskChanges Spatial Decision Support System

NASA Astrophysics Data System (ADS)

Olyazadeh, Roya; Jaboyedoff, Michel; van Westen, Cees; Bakker, Wim

2015-04-01

Multi-Criteria Evaluation (MCE) module is one of the five modules of RiskChanges spatial decision support system. RiskChanges web-based platform aims to analyze changes in hydro-meteorological risk and provides tools for selecting the best risk reduction alternative. It is developed under CHANGES framework (changes-itn.eu) and INCREO project (increo-fp7.eu). MCE tool helps decision makers and spatial planners to evaluate, sort and rank the decision alternatives. The users can choose among different indicators that are defined within the system using Risk and Cost Benefit analysis results besides they can add their own indicators. Subsequently the system standardizes and prioritizes them. Finally, the best decision alternative is selected by using the weighted sum model (WSM). The Application of this work is to facilitate the effect of MCE for analyzing changing risk over the time under different scenarios and future years by adopting a group decision making into practice and comparing the results by numeric and graphical view within the system. We believe that this study helps decision-makers to achieve the best solution by expressing their preferences for strategies under future scenarios. Keywords: Multi-Criteria Evaluation, Spatial Decision Support System, Weighted Sum Model, Natural Hazard Risk Management

The ACC/AHA 2013 pooled cohort equations compared to a Korean Risk Prediction Model for atherosclerotic cardiovascular disease.

PubMed

Jung, Keum Ji; Jang, Yangsoo; Oh, Dong Joo; Oh, Byung-Hee; Lee, Sang Hoon; Park, Seong-Wook; Seung, Ki-Bae; Kim, Hong-Kyu; Yun, Young Duk; Choi, Sung Hee; Sung, Jidong; Lee, Tae-Yong; Kim, Sung Hi; Koh, Sang Baek; Kim, Moon Chan; Chang Kim, Hyeon; Kimm, Heejin; Nam, Chungmo; Park, Sungha; Jee, Sun Ha

2015-09-01

To evaluate the performance of the American College of Cardiology/American Heart Association (ACC/AHA) 2013 Pooled Cohort Equations in the Korean Heart Study (KHS) population and to develop a Korean Risk Prediction Model (KRPM) for atherosclerotic cardiovascular disease (ASCVD) events. The KHS cohort included 200,010 Korean adults aged 40-79 years who were free from ASCVD at baseline. Discrimination, calibration, and recalibration of the ACC/AHA Equations in predicting 10-year ASCVD risk in the KHS cohort were evaluated. The KRPM was derived using Cox model coefficients, mean risk factor values, and mean incidences from the KHS cohort. In the discriminatory analysis, the ACC/AHA Equations' White and African-American (AA) models moderately distinguished cases from non-cases, and were similar to the KRPM: For men, the area under the receiver operating characteristic curve (AUROCs) were 0.727 (White model), 0.725 (AA model), and 0.741 (KRPM); for women, the corresponding AUROCs were 0.738, 0.739, and 0.745. Absolute 10-year ASCVD risk for men in the KHS cohort was overestimated by 56.5% (White model) and 74.1% (AA model), while the risk for women was underestimated by 27.9% (White model) and overestimated by 29.1% (AA model). Recalibration of the ACC/AHA Equations did not affect discriminatory ability but improved calibration substantially, especially in men in the White model. Of the three ASCVD risk prediction models, the KRPM showed best calibration. The ACC/AHA Equations should not be directly applied for ASCVD risk prediction in a Korean population. The KRPM showed best predictive ability for ASCVD risk. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil.

PubMed

Lowe, Rachel; Coelho, Caio As; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

2016-02-24

Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.

Risk Assessment on Constructors during Over-water Riprap Based on Entropy Weight and FAHP

NASA Astrophysics Data System (ADS)

Wu, Tongqing; Li, Liang; Liang, Zelong; Mao, Tian; Shao, Weifeng

2017-07-01

Being aimed at waterway regulation engineering, there exist risks of over-water riprap for constructors which keeps uncertainty and complexity. For the purpose of evaluating the possibility and consequence, this paper utilizes fuzzy analytic hierarchy process with abbreviation of FAHP to do empowerment on the related risk indicators, constructs FAHP under entropy weight and establishes relevant evaluation factor set and evaluation language for constructors during over-water riprap construction process. Through doing risk probability estimation and risk consequence size evaluation on the factor of constructors, this paper introduces this model into risk analysis on constructors during over-water riprap of Ching River waterway regulation project. Results show that evaluation of this method is so credible that it could be utilized in practical engineering.

Risk in fire management decisionmaking: techniques and criteria

Treesearch

Gail Blatternberger; William F. Hyde; Thomas J. Mills

1984-01-01

In the past, decisionmaking in wildland fire management generally has not included a full consideration of the risk and uncertainty that is inherent in evaluating alternatives. Fire management policies in some Federal land management agencies now require risk evaluation. The model for estimating the economic efficiency of fire program alternatives is the minimization...

Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci

PubMed Central

Clyde, Merlise A.; Palmieri Weber, Rachel; Iversen, Edwin S.; Poole, Elizabeth M.; Doherty, Jennifer A.; Goodman, Marc T.; Ness, Roberta B.; Risch, Harvey A.; Rossing, Mary Anne; Terry, Kathryn L.; Wentzensen, Nicolas; Whittemore, Alice S.; Anton-Culver, Hoda; Bandera, Elisa V.; Berchuck, Andrew; Carney, Michael E.; Cramer, Daniel W.; Cunningham, Julie M.; Cushing-Haugen, Kara L.; Edwards, Robert P.; Fridley, Brooke L.; Goode, Ellen L.; Lurie, Galina; McGuire, Valerie; Modugno, Francesmary; Moysich, Kirsten B.; Olson, Sara H.; Pearce, Celeste Leigh; Pike, Malcolm C.; Rothstein, Joseph H.; Sellers, Thomas A.; Sieh, Weiva; Stram, Daniel; Thompson, Pamela J.; Vierkant, Robert A.; Wicklund, Kristine G.; Wu, Anna H.; Ziogas, Argyrios; Tworoger, Shelley S.; Schildkraut, Joellen M.

2016-01-01

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted. PMID:27698005

Visualization of risk of radiogenic second cancer in the organs and tissues of the human body.

PubMed

Zhang, Rui; Mirkovic, Dragan; Newhauser, Wayne D

2015-04-28

Radiogenic second cancer is a common late effect in long term cancer survivors. Currently there are few methods or tools available to visually evaluate the spatial distribution of risks of radiogenic late effects in the human body. We developed a risk visualization method and demonstrated it for radiogenic second cancers in tissues and organs of one patient treated with photon volumetric modulated arc therapy and one patient treated with proton craniospinal irradiation. Treatment plans were generated using radiotherapy treatment planning systems (TPS) and dose information was obtained from TPS. Linear non-threshold risk coefficients for organs at risk of second cancer incidence were taken from the Biological Effects of Ionization Radiation VII report. Alternative risk models including linear exponential model and linear plateau model were also examined. The predicted absolute lifetime risk distributions were visualized together with images of the patient anatomy. The risk distributions of second cancer for the two patients were visually presented. The risk distributions varied with tissue, dose, dose-risk model used, and the risk distribution could be similar to or very different from the dose distribution. Our method provides a convenient way to directly visualize and evaluate the risks of radiogenic second cancer in organs and tissues of the human body. In the future, visual assessment of risk distribution could be an influential determinant for treatment plan scoring.

Two criteria for evaluating risk prediction models

PubMed Central

Pfeiffer, R.M.; Gail, M.H.

2010-01-01

SUMMARY We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF(q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF(p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF(q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF(p) assess the feasibility of covering 100p% of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data. PMID:21155746

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…

Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 3: An Evaluation of SNP Incorporated National Health Information System of Turkey for Prostate Cancer

PubMed Central

Beyan, Timur

2014-01-01

Background A personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world, including Turkey. Objective As an initial attempt to develop a sophisticated infrastructure, we have concentrated on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluated the performance of various predictive models for prostate cancer cases. We present our work as a three part miniseries: (1) an overview of requirements, (2) the incorporation of SNP data into the NHIS-T, and (3) an evaluation of SNP data incorporated into the NHIS-T for prostate cancer. Methods In the third article of this miniseries, we have evaluated the proposed complementary capabilities (ie, knowledge base and end-user application) with real data. Before the evaluation phase, clinicogenomic associations about increased prostate cancer risk were extracted from knowledge sources, and published predictive genomic models assessing individual prostate cancer risk were collected. To evaluate complementary capabilities, we also gathered personal SNP data of four prostate cancer cases and fifteen controls. Using these data files, we compared various independent and model-based, prostate cancer risk assessment approaches. Results Through the extraction and selection processes of SNP-prostate cancer risk associations, we collected 209 independent associations for increased risk of prostate cancer from the studied knowledge sources. Also, we gathered six cumulative models and two probabilistic models. Cumulative models and assessment of independent associations did not have impressive results. There was one of the probabilistic, model-based interpretation that was successful compared to the others. In envirobehavioral and clinical evaluations, we found that some of the comorbidities, especially, would be useful to evaluate disease risk. Even though we had a very limited dataset, a comparison of performances of different disease models and their implementation with real data as use case scenarios helped us to gain deeper insight into the proposed architecture. Conclusions In order to benefit from genomic variation data, existing EHR/EMR systems must be constructed with the capability of tracking and monitoring all aspects of personal health status (genomic, clinical, environmental, etc) in 24/7 situations, and also with the capability of suggesting evidence-based recommendations. A national-level, accredited knowledge base is a top requirement for improved end-user systems interpreting these parameters. Finally, categorization using similar, individual characteristics (SNP patterns, exposure history, etc) may be an effective way to predict disease risks, but this approach needs to be concretized and supported with new studies. PMID:25600087

Impact of model-based risk analysis for liver surgery planning.

PubMed

Hansen, C; Zidowitz, S; Preim, B; Stavrou, G; Oldhafer, K J; Hahn, H K

2014-05-01

A model-based risk analysis for oncologic liver surgery was described in previous work (Preim et al. in Proceedings of international symposium on computer assisted radiology and surgery (CARS), Elsevier, Amsterdam, pp. 353–358, 2002; Hansen et al. Int I Comput Assist Radiol Surg 4(5):469–474, 2009). In this paper, we present an evaluation of this method. To prove whether and how the risk analysis facilitates the process of liver surgery planning, an explorative user study with 10 liver experts was conducted. The purpose was to compare and analyze their decision-making. The results of the study show that model-based risk analysis enhances the awareness of surgical risk in the planning stage. Participants preferred smaller resection volumes and agreed more on the safety margins’ width in case the risk analysis was available. In addition, time to complete the planning task and confidence of participants were not increased when using the risk analysis. This work shows that the applied model-based risk analysis may influence important planning decisions in liver surgery. It lays a basis for further clinical evaluations and points out important fields for future research.

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.

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.

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.

Lessons learned using different mouse models during space radiation-induced lung tumorigenesis experiments

NASA Astrophysics Data System (ADS)

Wang, Jian; Zhang, Xiangming; Wang, Ping; Wang, Xiang; Farris, Alton B.; Wang, Ya

2016-06-01

Unlike terrestrial ionizing radiation, space radiation, especially galactic cosmic rays (GCR), contains high energy charged (HZE) particles with high linear energy transfer (LET). Due to a lack of epidemiologic data for high-LET radiation exposure, it is highly uncertain how high the carcinogenesis risk is for astronauts following exposure to space radiation during space missions. Therefore, using mouse models is necessary to evaluate the risk of space radiation-induced tumorigenesis; however, which mouse model is better for these studies remains uncertain. Since lung tumorigenesis is the leading cause of cancer death among both men and women, and low-LET radiation exposure increases human lung carcinogenesis, evaluating space radiation-induced lung tumorigenesis is critical to enable safe Mars missions. Here, by comparing lung tumorigenesis obtained from different mouse strains, as well as miR-21 in lung tissue/tumors and serum, we believe that wild type mice with a low spontaneous tumorigenesis background are ideal for evaluating the risk of space radiation-induced lung tumorigenesis, and circulating miR-21 from such mice model might be used as a biomarker for predicting the risk.

Technical Guidelines for Environmental Dredging of Contaminated Sediments

DTIC Science & Technology

2008-09-01

health and ecological risk assessments . • Evaluate the need for and effectiveness of source control. • Evaluate potential remedies. • Document... risk , resource damage assessments , remedy selec- tion, and remedy design). It is therefore important to consult as many data users as possible (e.g...Contaminated Sediment Risks at Hazardous Waste Sites (USEPA 2002a). Risk Management Prin- ciple Number 4 is: “Develop and refine a conceptual site model that

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...

[Health risk assessment of coke oven PAHs emissions].

PubMed

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

2014-07-01

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

Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.

PubMed

Schummers, Laura; Himes, Katherine P; Bodnar, Lisa M; Hutcheon, Jennifer A

2016-09-21

Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r 2 ) for each model. Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.

A fuzzy linguistic model for the prediction of carpal tunnel syndrome risks in an occupational environment.

PubMed

Bell, P M; Crumpton, L

1997-08-01

This research presents the development and evaluation of a fuzzy linguistic model designated to predict the risk of carpal tunnel syndrome (CTS) in an occupational setting. CTS has become one of the largest problems facing ergonomists and the medical community because it is developing in epidemic proportions within the occupational environment. In addition, practitioners are interested in identifying accurate methods for evaluating the risk of CTS in an occupational setting. It is hypothesized that many factors impact an individual's likelihood of developing CTS and the eventual development of CTS. This disparity in the occurrence of CTS for workers with similar backgrounds and work activities has confused researchers and has been a stumbling block in the development of a model for widespread use in evaluating the development of CTS. Thus this research is an attempt to develop a method that can be used to predict the likelihood of CTS risk in a variety of environments. The intent is that this model will be applied eventually in an occupational setting, thus model development was focused on a method that provided a usable interface and the desired system inputs can also be obtained without the benefit of a medical practitioner. The methodology involves knowledge acquisition to identify and categorize a holistic set of risk factors that include task-related, personal, and organizational categories. The determination of relative factor importance was accomplished using analytic hierarchy processing (AHP) analysis. Finally a mathematical representation of the CTS risk was accomplished by utilizing fuzzy set theory in order to quantify linguistic input parameters. An evaluation of the model including determination of sensitivity and specificity is conducted and the results of the model indicate that the results are fairly accurate and this method has the potential for widespread use. A significant aspect of this research is the comparison of this technique to other methods for assessing presence of CTS. The results of this evaluation technique are compared with more traditional methods for assessing the presence of CTS.

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs

PubMed Central

McCarthy, John F.; Katz, Ira R.; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M.; Nielson, Christopher; Schoenbaum, Michael

2015-01-01

Objectives. The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Methods. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Results. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Conclusions. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions. PMID:26066914

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs.

PubMed

McCarthy, John F; Bossarte, Robert M; Katz, Ira R; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M; Nielson, Christopher; Schoenbaum, Michael

2015-09-01

The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions.

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

PubMed

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

2017-02-01

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

A wildfire risk modeling system for evaluating landscape fuel treatment strategies

Treesearch

Alan Ager; Mark Finney; Andrew McMahan

2006-01-01

Despite a wealth of literature and models concerning wildfire risk, field units in Federal land management agencies lack a clear framework and operational tools to measure how risk might change from proposed fuel treatments. In an actuarial context, risk is defined as the expected value change from a fire, calculated as the product of (1) probability of a fire at a...

Are cardiovascular disease risk assessment and management programmes cost effective? A systematic review of the evidence.

PubMed

Lee, John Tayu; Lawson, Kenny D; Wan, Yizhou; Majeed, Azeem; Morris, Stephen; Soljak, Michael; Millett, Christopher

2017-06-01

The World Health Organization recommends that countries implement population-wide cardiovascular disease (CVD) risk assessment and management programmes. The aim of this study was to conduct a systematic review to evaluate whether this recommendation is supported by cost-effectiveness evidence. Published economic evaluations were identified via electronic medical and social science databases (including Medline, Web of Science, and the NHS Economic Evaluation Database) from inception to March 2016. Study quality was evaluated using a modified version of the Consolidated Health Economic Evaluation Reporting Standards. Fourteen economic evaluations were included: five studies based on randomised controlled trials, seven studies based on observational studies and two studies using hypothetical modelling synthesizing secondary data. Trial based studies measured CVD risk factor changes over 1 to 3years, with modelled projections of longer term events. Programmes were either not, or only, cost-effective under non-verified assumptions such as sustained risk factor changes. Most observational and hypothetical studies suggested programmes were likely to be cost-effective; however, study deigns are subject to bias and subsequent empirical evidence has contradicted key assumptions. No studies assessed impacts on inequalities. In conclusion, recommendations for population-wide risk assessment and management programmes lack a robust, real world, evidence basis. Given implementation is resource intensive there is a need for robust economic evaluation, ideally conducted alongside trials, to assess cost effectiveness. Further, the efficiency and equity impact of different delivery models should be investigated, and also the combination of targeted screening with whole population interventions recognising that there multiple approaches to prevention. Copyright © 2017. Published by Elsevier Inc.

An integrated epidemiological and neural net model of the warfarin effect in managed care patients.

PubMed

Jacobs, David M; Stefanovic, Filip; Wilton, Greg; Gomez-Caminero, Andres; Schentag, Jerome J

2017-01-01

Risk assessment tools are utilized to estimate the risk for stroke and need of anticoagulation therapy for patients with atrial fibrillation (AF). These risk stratification scores are limited by the information inputted into them and a reliance on time-independent variables. The objective of this study was to develop a time-dependent neural net model to identify AF populations at high risk of poor clinical outcomes and evaluate the discriminatory ability of the model in a managed care population. We performed a longitudinal, cohort study within a health-maintenance organization from 1997 to 2008. Participants were identified with incident AF irrespective of warfarin status and followed through their duration within the database. Three clinical outcome measures were evaluated including stroke, myocardial infarction, and hemorrhage. A neural net model was developed to identify patients at high risk of clinical events and defined to be an "enriched" patient. The model defines the enrichment based on the top 10 minimum mean square error output parameters that describe the three clinical outcomes. Cox proportional hazard models were utilized to evaluate the outcome measures. Among 285 patients, the mean age was 74±12 years with a mean follow-up of 4.3±2.6 years, and 154 (54%) were treated with warfarin. After propensity score adjustment, warfarin use was associated with a slightly increased risk of adverse outcomes (including stroke, myocardial infarction, and hemorrhage), though it did not attain statistical significance (adjusted hazard ratio [aHR] =1.22; 95% confidence interval [CI] 0.75-1.97; p =0.42). Within the neural net model, subjects at high risk of adverse outcomes were identified and labeled as "enriched." Following propensity score adjustment, enriched subjects were associated with an 81% higher risk of adverse outcomes as compared to nonenriched subjects (aHR=1.81; 95% CI, 1.15-2.88; p =0.01). Enrichment methodology improves the statistical discrimination of meaningful endpoints when used in a health records-based analysis.

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.

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 ...

Risk assessment model for development of advanced age-related macular degeneration.

PubMed

Klein, Michael L; Francis, Peter J; Ferris, Frederick L; Hamon, Sara C; Clemons, Traci E

2011-12-01

To design a risk assessment model for development of advanced age-related macular degeneration (AMD) incorporating phenotypic, demographic, environmental, and genetic risk factors. We evaluated longitudinal data from 2846 participants in the Age-Related Eye Disease Study. At baseline, these individuals had all levels of AMD, ranging from none to unilateral advanced AMD (neovascular or geographic atrophy). Follow-up averaged 9.3 years. We performed a Cox proportional hazards analysis with demographic, environmental, phenotypic, and genetic covariates and constructed a risk assessment model for development of advanced AMD. Performance of the model was evaluated using the C statistic and the Brier score and externally validated in participants in the Complications of Age-Related Macular Degeneration Prevention Trial. The final model included the following independent variables: age, smoking history, family history of AMD (first-degree member), phenotype based on a modified Age-Related Eye Disease Study simple scale score, and genetic variants CFH Y402H and ARMS2 A69S. The model did well on performance measures, with very good discrimination (C statistic = 0.872) and excellent calibration and overall performance (Brier score at 5 years = 0.08). Successful external validation was performed, and a risk assessment tool was designed for use with or without the genetic component. We constructed a risk assessment model for development of advanced AMD. The model performed well on measures of discrimination, calibration, and overall performance and was successfully externally validated. This risk assessment tool is available for online use.

Evaluation of agricultural nonpoint source pollution potential risk over China with a Transformed-Agricultural Nonpoint Pollution Potential Index method.

PubMed

Yang, Fei; Xu, Zhencheng; Zhu, Yunqiang; He, Chansheng; Wu, Genyi; Qiu, Jin Rong; Fu, Qiang; Liu, Qingsong

2013-01-01

Agricultural nonpoint source (NPS) pollution has been the most important threat to water environment quality. Understanding the spatial distribution of NPS pollution potential risk is important for taking effective measures to control and reduce NPS pollution. A Transformed-Agricultural Nonpoint Pollution Potential Index (T-APPI) model was constructed for evaluating the national NPS pollution potential risk in this study; it was also combined with remote sensing and geographic information system techniques for evaluation on the large scale and at 1 km2 spatial resolution. This model considers many factors contributing to the NPS pollution as the original APPI model, summarized as four indicators of the runoff, sediment production, chemical use and the people and animal load. These four indicators were analysed in detail at 1 km2 spatial resolution throughout China. The T-APPI model distinguished the four indicators into pollution source factors and transport process factors; it also took their relationship into consideration. The studied results showed that T-APPI is a credible and convenient method for NPS pollution potential risk evaluation. The results also indicated that the highest NPS pollution potential risk is distributed in the middle-southern Jiangsu province. Several other regions, including the North China Plain, Chengdu Basin Plain, Jianghan Plain, cultivated lands in Guangdong and Guangxi provinces, also showed serious NPS pollution potential. This study can provide a scientific reference for predicting the future NPS pollution risk throughout China and may be helpful for taking reasonable and effective measures for preventing and controlling NPS pollution.

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.

Estimation of value at risk and conditional value at risk using normal mixture distributions model

NASA Astrophysics Data System (ADS)

Kamaruzzaman, Zetty Ain; Isa, Zaidi

2013-04-01

Normal mixture distributions model has been successfully applied in financial time series analysis. In this paper, we estimate the return distribution, value at risk (VaR) and conditional value at risk (CVaR) for monthly and weekly rates of returns for FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI) from July 1990 until July 2010 using the two component univariate normal mixture distributions model. First, we present the application of normal mixture distributions model in empirical finance where we fit our real data. Second, we present the application of normal mixture distributions model in risk analysis where we apply the normal mixture distributions model to evaluate the value at risk (VaR) and conditional value at risk (CVaR) with model validation for both risk measures. The empirical results provide evidence that using the two components normal mixture distributions model can fit the data well and can perform better in estimating value at risk (VaR) and conditional value at risk (CVaR) where it can capture the stylized facts of non-normality and leptokurtosis in returns distribution.

Evaluation of the Occupational Risks from Jet Fuel (Toxicity Screening Battery)

DTIC Science & Technology

2012-09-01

1α may serve as a marker of epidermal damage or stress due to irritation in this in vitro model. As an alternative to the 3-dimensional human skin...AFRL-RH-FS-SR-2013-0003 Final Report: Evaluation of the Occupational Risks from Jet Fuel (Toxicity Screening Battery) David R. Mattie...2. REPORT TYPE Special Report 3. DATES COVERED (From - To) Oct 2010 – Dec 2011 4. TITLE AND SUBTITLE Evaluation of the Occupational Risks from

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Suicide Attempt Risk in Youths: Utility of the Harkavy-Asnis Suicide Scale for Monitoring Risk Levels

ERIC Educational Resources Information Center

Asarnow, Joan; McArthur, David; Hughes, Jennifer; Barbery, Veronica; Berk, Michele

2012-01-01

The Harkavy-Asnis Suicide Scale (HASS), one of the few self-report scales assessing suicidal behavior was evaluated and ideation, was evaluated and predictors of suicide attempts (SAs) were identified with the goal of developing a model that clinicians can use for monitoring SA risk. Participants were 131 pediatric emergency department (ED)…

Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil

PubMed Central

Lowe, Rachel; Coelho, Caio AS; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

2016-01-01

Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. DOI: http://dx.doi.org/10.7554/eLife.11285.001 PMID:26910315

America's Youth Are at Risk: Developing Models for Action in the Nation's Public Libraries.

ERIC Educational Resources Information Center

Flum, Judith G.; Weisner, Stan

1993-01-01

Discussion of public library support systems for at-risk teens focuses on the Bay Area Library and Information System (BALIS) that was developed to improve library services to at-risk teenagers in the San Francisco Bay area. Highlights include needs assessment; staff training; intervention models; and project evaluation. (10 references) (LRW)

Evaluating biomarkers to model cancer risk post cosmic ray exposure

PubMed Central

Sridhara, Deepa M.; Asaithamby, Aroumougame; Blattnig, Steve R.; Costes, Sylvain V.; Doetsch, Paul W.; Dynan, William S.; Hahnfeldt, Philip; Hlatky, Lynn; Kidane, Yared; Kronenberg, Amy; Naidu, Mamta D.; Peterson, Leif E.; Plante, Ianik; Ponomarev, Artem L.; Saha, Janapriya; Snijders, Antoine M.; Srinivasan, Kalayarasan; Tang, Jonathan; Werner, Erica; Pluth, Janice M.

2017-01-01

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens. PMID:27345199

Evaluating biomarkers to model cancer risk post cosmic ray exposure

NASA Astrophysics Data System (ADS)

Sridharan, Deepa M.; Asaithamby, Aroumougame; Blattnig, Steve R.; Costes, Sylvain V.; Doetsch, Paul W.; Dynan, William S.; Hahnfeldt, Philip; Hlatky, Lynn; Kidane, Yared; Kronenberg, Amy; Naidu, Mamta D.; Peterson, Leif E.; Plante, Ianik; Ponomarev, Artem L.; Saha, Janapriya; Snijders, Antoine M.; Srinivasan, Kalayarasan; Tang, Jonathan; Werner, Erica; Pluth, Janice M.

2016-06-01

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens.

Evaluating biomarkers to model cancer risk post cosmic ray exposure.

PubMed

Sridharan, Deepa M; Asaithamby, Aroumougame; Blattnig, Steve R; Costes, Sylvain V; Doetsch, Paul W; Dynan, William S; Hahnfeldt, Philip; Hlatky, Lynn; Kidane, Yared; Kronenberg, Amy; Naidu, Mamta D; Peterson, Leif E; Plante, Ianik; Ponomarev, Artem L; Saha, Janapriya; Snijders, Antoine M; Srinivasan, Kalayarasan; Tang, Jonathan; Werner, Erica; Pluth, Janice M

2016-06-01

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens. Copyright © 2016 The Committee on Space Research (COSPAR). All rights reserved.

Evaluation of portfolio credit risk based on survival analysis for progressive censored data

NASA Astrophysics Data System (ADS)

Jaber, Jamil J.; Ismail, Noriszura; Ramli, Siti Norafidah Mohd

2017-04-01

In credit risk management, the Basel committee provides a choice of three approaches to the financial institutions for calculating the required capital: the standardized approach, the Internal Ratings-Based (IRB) approach, and the Advanced IRB approach. The IRB approach is usually preferred compared to the standard approach due to its higher accuracy and lower capital charges. This paper use several parametric models (Exponential, log-normal, Gamma, Weibull, Log-logistic, Gompertz) to evaluate the credit risk of the corporate portfolio in the Jordanian banks based on the monthly sample collected from January 2010 to December 2015. The best model is selected using several goodness-of-fit criteria (MSE, AIC, BIC). The results indicate that the Gompertz distribution is the best model parametric model for the data.

Evaluation of Enhanced Risk Monitors for Use on Advanced Reactors

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

Ramuhalli, Pradeep; Veeramany, Arun; Bonebrake, Christopher A.

This study provides an overview of the methodology for integrating time-dependent failure probabilities into nuclear power reactor risk monitors. This prototypic enhanced risk monitor (ERM) methodology was evaluated using a hypothetical probabilistic risk assessment (PRA) model, generated using a simplified design of a liquid-metal-cooled advanced reactor (AR). Component failure data from industry compilation of failures of components similar to those in the simplified AR model were used to initialize the PRA model. Core damage frequency (CDF) over time were computed and analyzed. In addition, a study on alternative risk metrics for ARs was conducted. Risk metrics that quantify the normalizedmore » cost of repairs, replacements, or other operations and management (O&M) actions were defined and used, along with an economic model, to compute the likely economic risk of future actions such as deferred maintenance based on the anticipated change in CDF due to current component condition and future anticipated degradation. Such integration of conventional-risk metrics with alternate-risk metrics provides a convenient mechanism for assessing the impact of O&M decisions on safety and economics of the plant. It is expected that, when integrated with supervisory control algorithms, such integrated-risk monitors will provide a mechanism for real-time control decision-making that ensure safety margins are maintained while operating the plant in an economically viable manner.« less

Validation of Fatigue Modeling Predictions in Aviation Operations

NASA Technical Reports Server (NTRS)

Gregory, Kevin; Martinez, Siera; Flynn-Evans, Erin

2017-01-01

Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.

[Study on building index system of risk assessment of post-marketing Chinese patent medicine based on AHP-fuzzy neural network].

PubMed

Li, Yuanyuan; Xie, Yanming; Fu, Yingkun

2011-10-01

Currently massive researches have been launched about the safety, efficiency and economy of post-marketing Chinese patent medicine (CPM) proprietary Chinese medicine, but it was lack of a comprehensive interpretation. Establishing the risk evaluation index system and risk assessment model of CPM is the key to solve drug safety problems and protect people's health. The clinical risk factors of CPM exist similarities with the Western medicine, can draw lessons from foreign experience, but also have itself multi-factor multivariate multi-level complex features. Drug safety risk assessment for the uncertainty and complexity, using analytic hierarchy process (AHP) to empower the index weights, AHP-based fuzzy neural network to build post-marketing CPM risk evaluation index system and risk assessment model and constantly improving the application of traditional Chinese medicine characteristic is accord with the road and feasible beneficial exploration.

Financial Crisis: A New Measure for Risk of Pension Fund Portfolios

PubMed Central

Cadoni, Marinella; Melis, Roberta; Trudda, Alessandro

2015-01-01

It has been argued that pension funds should have limitations on their asset allocation, based on the risk profile of the different financial instruments available on the financial markets. This issue proves to be highly relevant at times of market crisis, when a regulation establishing limits to risk taking for pension funds could prevent defaults. In this paper we present a framework for evaluating the risk level of a single financial instrument or a portfolio. By assuming that the log asset returns can be described by a multifractional Brownian motion, we evaluate the risk using the time dependent Hurst parameter H(t) which models volatility. To provide a measure of the risk, we model the Hurst parameter with a random variable with mixture of beta distribution. We prove the efficacy of the methodology by implementing it on different risk level financial instruments and portfolios. PMID:26086529

Financial Crisis: A New Measure for Risk of Pension Fund Portfolios.

PubMed

Cadoni, Marinella; Melis, Roberta; Trudda, Alessandro

2015-01-01

It has been argued that pension funds should have limitations on their asset allocation, based on the risk profile of the different financial instruments available on the financial markets. This issue proves to be highly relevant at times of market crisis, when a regulation establishing limits to risk taking for pension funds could prevent defaults. In this paper we present a framework for evaluating the risk level of a single financial instrument or a portfolio. By assuming that the log asset returns can be described by a multifractional Brownian motion, we evaluate the risk using the time dependent Hurst parameter H(t) which models volatility. To provide a measure of the risk, we model the Hurst parameter with a random variable with mixture of beta distribution. We prove the efficacy of the methodology by implementing it on different risk level financial instruments and portfolios.

Cardiovascular risk prediction in HIV-infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score (ASCVD), Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) and Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) risk prediction models.

PubMed

Krikke, M; Hoogeveen, R C; Hoepelman, A I M; Visseren, F L J; Arends, J E

2016-04-01

The aim of the study was to compare the predictions of five popular cardiovascular disease (CVD) risk prediction models, namely the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, the Framingham Heart Study (FHS) coronary heart disease (FHS-CHD) and general CVD (FHS-CVD) models, the American Heart Association (AHA) atherosclerotic cardiovascular disease risk score (ASCVD) model and the Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) model. A cross-sectional design was used to compare the cumulative CVD risk predictions of the models. Furthermore, the predictions of the general CVD models were compared with those of the HIV-specific D:A:D model using three categories (< 10%, 10-20% and > 20%) to categorize the risk and to determine the degree to which patients were categorized similarly or in a higher/lower category. A total of 997 HIV-infected patients were included in the study: 81% were male and they had a median age of 46 [interquartile range (IQR) 40-52] years, a known duration of HIV infection of 6.8 (IQR 3.7-10.9) years, and a median time on ART of 6.4 (IQR 3.0-11.5) years. The D:A:D, ASCVD and SCORE-NL models gave a lower cumulative CVD risk, compared with that of the FHS-CVD and FHS-CHD models. Comparing the general CVD models with the D:A:D model, the FHS-CVD and FHS-CHD models only classified 65% and 79% of patients, respectively, in the same category as did the D:A:D model. However, for the ASCVD and SCORE-NL models, this percentage was 89% and 87%, respectively. Furthermore, FHS-CVD and FHS-CHD attributed a higher CVD risk to 33% and 16% of patients, respectively, while this percentage was < 6% for ASCVD and SCORE-NL. When using FHS-CVD and FHS-CHD, a higher overall CVD risk was attributed to the HIV-infected patients than when using the D:A:D, ASCVD and SCORE-NL models. This could have consequences regarding overtreatment, drug-related adverse events and drug-drug interactions. © 2015 British HIV Association.

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

PubMed

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

2016-01-01

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

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

PubMed

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

2016-04-01

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

CANCER RISK ASSESSMENTS (RA.D.1D)

EPA Science Inventory

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

A framework for quantifying net benefits of alternative prognostic models.

PubMed

Rapsomaniki, Eleni; White, Ian R; Wood, Angela M; Thompson, Simon G

2012-01-30

New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks. Copyright © 2011 John Wiley & Sons, Ltd.

Evaluation of soil erosion risk using Analytic Network Process and GIS: a case study from Spanish mountain olive plantations.

PubMed

Nekhay, Olexandr; Arriaza, Manuel; Boerboom, Luc

2009-07-01

The study presents an approach that combined objective information such as sampling or experimental data with subjective information such as expert opinions. This combined approach was based on the Analytic Network Process method. It was applied to evaluate soil erosion risk and overcomes one of the drawbacks of USLE/RUSLE soil erosion models, namely that they do not consider interactions among soil erosion factors. Another advantage of this method is that it can be used if there are insufficient experimental data. The lack of experimental data can be compensated for through the use of expert evaluations. As an example of the proposed approach, the risk of soil erosion was evaluated in olive groves in Southern Spain, showing the potential of the ANP method for modelling a complex physical process like soil erosion.

Building a Better Model: A Comprehensive Breast Cancer Risk Model Incorporating Breast Density to Stratify Risk and Apply Resources

DTIC Science & Technology

2014-10-01

variability with well trained readers. Figure 7: comparison between the PD (percent density using Cumulus area) and the automatic PD. The...evaluation of outlier correction, comparison of several different software methods, precision measurement, and evaluation of variation by mammography...chart review for selected cases (month 4-6). Comparison of information from the Breast Cancer Database and medical records showed good consistency

The use of genetically modified mice in cancer risk assessment: challenges and limitations.

PubMed

Eastmond, David A; Vulimiri, Suryanarayana V; French, John E; Sonawane, Babasaheb

2013-09-01

The use of genetically modified (GM) mice to assess carcinogenicity is playing an increasingly important role in the safety evaluation of chemicals. While progress has been made in developing and evaluating mouse models such as the Trp53⁺/⁻, Tg.AC and the rasH2, the suitability of these models as replacements for the conventional rodent cancer bioassay and for assessing human health risks remains uncertain. The objective of this research was to evaluate the use of accelerated cancer bioassays with GM mice for assessing the potential health risks associated with exposure to carcinogenic agents. We compared the published results from the GM bioassays to those obtained in the National Toxicology Program's conventional chronic mouse bioassay for their potential use in risk assessment. Our analysis indicates that the GM models are less efficient in detecting carcinogenic agents but more consistent in identifying non-carcinogenic agents. We identified several issues of concern related to the design of the accelerated bioassays (e.g., sample size, study duration, genetic stability and reproducibility) as well as pathway-dependency of effects, and different carcinogenic mechanisms operable in GM and non-GM mice. The use of the GM models for dose-response assessment is particularly problematic as these models are, at times, much more or less sensitive than the conventional rodent cancer bioassays. Thus, the existing GM mouse models may be useful for hazard identification, but will be of limited use for dose-response assessment. Hence, caution should be exercised when using GM mouse models to assess the carcinogenic risks of chemicals.

The use of genetically modified mice in cancer risk assessment: Challenges and limitations*

PubMed Central

Eastmond, David A.; Vulimiri, Suryanarayana V.; French, John E.; Sonawane, Babasaheb

2015-01-01

The use of genetically modified (GM) mice to assess carcinogenicity is playing an increasingly important role in the safety evaluation of chemicals. While progress has been made in developing and evaluating mouse models such as the Trp53+/−, Tg.AC and the rasH2, the suitability of these models as replacements for the conventional rodent cancer bioassay and for assessing human health risks remains uncertain. The objective of this research was to evaluate the use of accelerated cancer bioassays with GM mice for assessing the potential health risks associated with exposure to carcinogenic agents. We compared the published results from the GM bioassays to those obtained in the National Toxicology Program’s conventional chronic mouse bioassay for their potential use in risk assessment. Our analysis indicates that the GM models are less efficient in detecting carcinogenic agents but more consistent in identifying non-carcinogenic agents. We identified several issues of concern related to the design of the accelerated bioassays (e.g., sample size, study duration, genetic stability and reproducibility) as well as pathway-dependency of effects, and different carcinogenic mechanisms operable in GM and non-GM mice. The use of the GM models for dose-response assessment is particularly problematic as these models are, at times, much more or less sensitive than the conventional rodent cancer bioassays. Thus, the existing GM mouse models may be useful for hazard identification, but will be of limited use for dose-response assessment. Hence, caution should be exercised when using GM mouse models to assess the carcinogenic risks of chemicals. PMID:23985072

RiskScape Volcano: Development of a risk assessment tool for volcanic hazards

NASA Astrophysics Data System (ADS)

Deligne, Natalia; King, Andrew; Jolly, Gill; Wilson, Grant; Wilson, Tom; Lindsay, Jan

2013-04-01

RiskScape is a multi-hazard risk assessment tool developed by GNS Science and the National Institute of Water and Atmospheric Research Ltd. (NIWA) in New Zealand that models the risk and impact of various natural hazards on a given built environment. RiskScape has a modular structure: the hazard module models hazard exposure (e.g., ash thickness at a given location), the asset module catalogues assets (built environment, infrastructure, and people) and their attributes exposed to the hazard, and the vulnerability module models the consequences of asset exposure to the hazard. Hazards presently included in RiskScape are earthquakes, river floods, tsunamis, windstorms, and ash from volcanic eruptions (specifically from Ruapehu). Here we present our framework for incorporating other volcanic hazards (e.g., pyroclastic density currents, lava flows, lahars, ground deformation) into RiskScape along with our approach for assessing asset vulnerability. We also will discuss the challenges of evaluating risk for 'point source' (e.g., stratovolcanoes) vs 'diffuse' (e.g., volcanic fields) volcanism using Ruapehu and the Auckland volcanic field as examples. Once operational, RiskScape Volcano will be a valuable resource both in New Zealand and internationally as a practical tool for evaluating risk and also as an example for how to predict the consequences of volcanic eruptions on both rural and urban environments.

Using Radiation Risk Models in Cancer Screening Simulations: Important Assumptions and Effects on Outcome Projections

PubMed Central

Lee, Janie M.; McMahon, Pamela M.; Lowry, Kathryn P.; Omer, Zehra B.; Eisenberg, Jonathan D.; Pandharipande, Pari V.; Gazelle, G. Scott

2012-01-01

Purpose: To evaluate the effect of incorporating radiation risk into microsimulation (first-order Monte Carlo) models for breast and lung cancer screening to illustrate effects of including radiation risk on patient outcome projections. Materials and Methods: All data used in this study were derived from publicly available or deidentified human subject data. Institutional review board approval was not required. The challenges of incorporating radiation risk into simulation models are illustrated with two cancer screening models (Breast Cancer Model and Lung Cancer Policy Model) adapted to include radiation exposure effects from mammography and chest computed tomography (CT), respectively. The primary outcome projected by the breast model was life expectancy (LE) for BRCA1 mutation carriers. Digital mammographic screening beginning at ages 25, 30, 35, and 40 years was evaluated in the context of screenings with false-positive results and radiation exposure effects. The primary outcome of the lung model was lung cancer–specific mortality reduction due to annual screening, comparing two diagnostic CT protocols for lung nodule evaluation. The Metropolis-Hastings algorithm was used to estimate the mean values of the results with 95% uncertainty intervals (UIs). Results: Without radiation exposure effects, the breast model indicated that annual digital mammography starting at age 25 years maximized LE (72.03 years; 95% UI: 72.01 years, 72.05 years) and had the highest number of screenings with false-positive results (2.0 per woman). When radiation effects were included, annual digital mammography beginning at age 30 years maximized LE (71.90 years; 95% UI: 71.87 years, 71.94 years) with a lower number of screenings with false-positive results (1.4 per woman). For annual chest CT screening of 50-year-old females with no follow-up for nodules smaller than 4 mm in diameter, the lung model predicted lung cancer–specific mortality reduction of 21.50% (95% UI: 20.90%, 22.10%) without radiation risk and 17.75% (95% UI: 16.97%, 18.41%) with radiation risk. Conclusion: Because including radiation exposure risk can influence long-term projections from simulation models, it is important to include these risks when conducting modeling-based assessments of diagnostic imaging. © RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11110352/-/DC1 PMID:22357897

Psychological first aid following trauma: implementation and evaluation framework for high-risk organizations.

PubMed

Forbes, David; Lewis, Virginia; Varker, Tracey; Phelps, Andrea; O'Donnell, Meaghan; Wade, Darryl J; Ruzek, Josef I; Watson, Patricia; Bryant, Richard A; Creamer, Mark

2011-01-01

International clinical practice guidelines for the management of psychological trauma recommend Psychological First Aid (PFA) as an early intervention for survivors of potentially traumatic events. These recommendations are consensus-based, and there is little published evidence assessing the effectiveness of PFA. This is not surprising given the nature of the intervention and the complicating factors involved in any evaluation of PFA. There is, nevertheless, an urgent need for stronger evidence evaluating its effectiveness. The current paper posits that the implementation and evaluation of PFA within high risk organizational settings is an ideal place to start. The paper provides a framework for a phasic approach to implementing PFA within such settings and presents a model for evaluating its effectiveness using a logic- or theory-based approach which considers both pre-event and post-event factors. Phases 1 and 2 of the PFA model are pre-event actions, and phases 3 and 4 are post-event actions. It is hoped that by using the Phased PFA model and evaluation method proposed in this paper, future researchers will begin to undertake the important task of building the evidence about the most effective approach to providing PFA in high risk organizational and community disaster settings.

Optimizing Surgical Quality Datasets to Care for Older Adults: Lessons from the American College of Surgeons NSQIP Geriatric Surgery Pilot.

PubMed

Berian, Julia R; Zhou, Lynn; Hornor, Melissa A; Russell, Marcia M; Cohen, Mark E; Finlayson, Emily; Ko, Clifford Y; Robinson, Thomas N; Rosenthal, Ronnie A

2017-12-01

Surgical quality datasets can be better tailored toward older adults. The American College of Surgeons (ACS) NSQIP Geriatric Surgery Pilot collected risk factors and outcomes in 4 geriatric-specific domains: cognition, decision-making, function, and mobility. This study evaluated the contributions of geriatric-specific factors to risk adjustment in modeling 30-day outcomes and geriatric-specific outcomes (postoperative delirium, new mobility aid use, functional decline, and pressure ulcers). Using ACS NSQIP Geriatric Surgery Pilot data (January 2014 to December 2016), 7 geriatric-specific risk factors were evaluated for selection in 14 logistic models (morbidities/mortality) in general-vascular and orthopaedic surgery subgroups. Hierarchical models evaluated 4 geriatric-specific outcomes, adjusting for hospitals-level effects and including Bayesian-type shrinkage, to estimate hospital performance. There were 36,399 older adults who underwent operations at 31 hospitals in the ACS NSQIP Geriatric Surgery Pilot. Geriatric-specific risk factors were selected in 10 of 14 models in both general-vascular and orthopaedic surgery subgroups. After risk adjustment, surrogate consent (odds ratio [OR] 1.5; 95% CI 1.3 to 1.8) and use of a mobility aid (OR 1.3; 95% CI 1.1 to 1.4) increased the risk for serious morbidity or mortality in the general-vascular cohort. Geriatric-specific factors were selected in all 4 geriatric-specific outcomes models. Rates of geriatric-specific outcomes were: postoperative delirium in 12.1% (n = 3,650), functional decline in 42.9% (n = 13,000), new mobility aid in 29.7% (n = 9,257), and new or worsened pressure ulcers in 1.7% (n = 527). Geriatric-specific risk factors are important for patient-centered care and contribute to risk adjustment in modeling traditional and geriatric-specific outcomes. To provide optimal patient care for older adults, surgical datasets should collect measures that address cognition, decision-making, mobility, and function. Copyright © 2017 American College of Surgeons. All rights reserved.

How TK-TD and population models for aquatic macrophytes could support the risk assessment for plant protection products.

PubMed

Hommen, Udo; Schmitt, Walter; Heine, Simon; Brock, Theo Cm; Duquesne, Sabine; Manson, Phil; Meregalli, Giovanna; Ochoa-Acuña, Hugo; van Vliet, Peter; Arts, Gertie

2016-01-01

This case study of the Society of Environmental Toxicology and Chemistry (SETAC) workshop MODELINK demonstrates the potential use of mechanistic effects models for macrophytes to extrapolate from effects of a plant protection product observed in laboratory tests to effects resulting from dynamic exposure on macrophyte populations in edge-of-field water bodies. A standard European Union (EU) risk assessment for an example herbicide based on macrophyte laboratory tests indicated risks for several exposure scenarios. Three of these scenarios are further analyzed using effect models for 2 aquatic macrophytes, the free-floating standard test species Lemna sp., and the sediment-rooted submerged additional standard test species Myriophyllum spicatum. Both models include a toxicokinetic (TK) part, describing uptake and elimination of the toxicant, a toxicodynamic (TD) part, describing the internal concentration-response function for growth inhibition, and a description of biomass growth as a function of environmental factors to allow simulating seasonal dynamics. The TK-TD models are calibrated and tested using laboratory tests, whereas the growth models were assumed to be fit for purpose based on comparisons of predictions with typical growth patterns observed in the field. For the risk assessment, biomass dynamics are predicted for the control situation and for several exposure levels. Based on specific protection goals for macrophytes, preliminary example decision criteria are suggested for evaluating the model outputs. The models refined the risk indicated by lower tier testing for 2 exposure scenarios, while confirming the risk associated for the third. Uncertainties related to the experimental and the modeling approaches and their application in the risk assessment are discussed. Based on this case study and the assumption that the models prove suitable for risk assessment once fully evaluated, we recommend that 1) ecological scenarios be developed that are also linked to the exposure scenarios, and 2) quantitative protection goals be set to facilitate the interpretation of model results for risk assessment. © 2015 SETAC.

Lessons learned using different mouse models during space radiation-induced lung tumorigenesis experiments.

PubMed

Wang, Jian; Zhang, Xiangming; Wang, Ping; Wang, Xiang; Farris, Alton B; Wang, Ya

2016-06-01

Unlike terrestrial ionizing radiation, space radiation, especially galactic cosmic rays (GCR), contains high energy charged (HZE) particles with high linear energy transfer (LET). Due to a lack of epidemiologic data for high-LET radiation exposure, it is highly uncertain how high the carcinogenesis risk is for astronauts following exposure to space radiation during space missions. Therefore, using mouse models is necessary to evaluate the risk of space radiation-induced tumorigenesis; however, which mouse model is better for these studies remains uncertain. Since lung tumorigenesis is the leading cause of cancer death among both men and women, and low-LET radiation exposure increases human lung carcinogenesis, evaluating space radiation-induced lung tumorigenesis is critical to enable safe Mars missions. Here, by comparing lung tumorigenesis obtained from different mouse strains, as well as miR-21 in lung tissue/tumors and serum, we believe that wild type mice with a low spontaneous tumorigenesis background are ideal for evaluating the risk of space radiation-induced lung tumorigenesis, and circulating miR-21 from such mice model might be used as a biomarker for predicting the risk. Copyright © 2016 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.

EVALUATING RISK-PREDICTION MODELS USING DATA FROM ELECTRONIC HEALTH RECORDS.

PubMed

Wang, L E; Shaw, Pamela A; Mathelier, Hansie M; Kimmel, Stephen E; French, Benjamin

2016-03-01

The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.

Credit Risk Evaluation of Large Power Consumers Considering Power Market Transaction

NASA Astrophysics Data System (ADS)

Fulin, Li; Erfeng, Xu; ke, Sun; Dunnan, Liu; Shuyi, Shen

2018-03-01

Large power users will participate in power market in various forms after power system reform. Meanwhile, great importance has always attached to the construction of the credit system in power industry. Due to the difference between the awareness of performance and the ability to perform, credit risk of power customer will emerge accordingly. Therefore, it is critical to evaluate credit risk of large power customers in the new situation of power market. Firstly, this paper constructs index system of credit risk of large power customers, and establishes evaluation model of interval number and AHP-entropy weight method.

Consumption of meat and fish and risk of lung cancer: results from the European Prospective Investigation into Cancer and Nutrition.

PubMed

Linseisen, Jakob; Rohrmann, Sabine; Bueno-de-Mesquita, Bas; Büchner, Frederike L; Boshuizen, Hendriek C; Agudo, Antonio; Gram, Inger Torhild; Dahm, Christina C; Overvad, Kim; Egeberg, Rikke; Tjønneland, Anne; Boeing, Heiner; Steffen, Annika; Kaaks, Rudolf; Lukanova, Annekatrin; Berrino, Franco; Palli, Domenico; Panico, Salvatore; Tumino, Rosario; Ardanaz, Eva; Dorronsoro, Miren; Huerta, José-Maria; Rodríguez, Laudina; Sánchez, María-José; Rasmuson, Torgny; Hallmans, Göran; Manjer, Jonas; Wirfält, Elisabet; Engeset, Dagrun; Skeie, Guri; Katsoulis, Michael; Oikonomou, Eleni; Trichopoulou, Antonia; Peeters, Petra H M; Khaw, Kay-Tee; Wareham, Nicholas; Allen, Naomi; Key, Tim; Brennan, Paul; Romieu, Isabelle; Slimani, Nadia; Vergnaud, Anne-Claire; Xun, Wei W; Vineis, Paolo; Riboli, Elio

2011-06-01

Evidence from case-control studies, but less so from cohort studies, suggests a positive association between meat intake and risk of lung cancer. Therefore, this association was evaluated in the frame of the European Prospective Investigation into Cancer and Nutrition, EPIC. Data from 478,021 participants, recruited from 10 European countries, who completed a dietary questionnaire in 1992-2000 were evaluated; 1,822 incident primary lung cancer cases were included in the present evaluation. Relative risk estimates were calculated for categories of meat intake using multi-variably adjusted Cox proportional hazard models. In addition, the continuous intake variables were calibrated by means of 24-h diet recall data to account for part of the measurement error. There were no consistent associations between meat consumption and the risk of lung cancer. Neither red meat (RR = 1.06, 95% CI 0.89-1.27 per 50 g intake/day; calibrated model) nor processed meat (RR = 1.13, 95% CI 0.95-1.34 per 50 g/day; calibrated model) was significantly related to an increased risk of lung cancer. Also, consumption of white meat and fish was not associated with the risk of lung cancer. These findings do not support the hypothesis that a high intake of red and processed meat is a risk factor for lung cancer.

Development of a Risk Prediction Model and Clinical Risk Score for Isolated Tricuspid Valve Surgery.

PubMed

LaPar, Damien J; Likosky, Donald S; Zhang, Min; Theurer, Patty; Fonner, C Edwin; Kern, John A; Bolling, Stephen F; Drake, Daniel H; Speir, Alan M; Rich, Jeffrey B; Kron, Irving L; Prager, Richard L; Ailawadi, Gorav

2018-02-01

While tricuspid valve (TV) operations remain associated with high mortality (∼8-10%), no robust prediction models exist to support clinical decision-making. We developed a preoperative clinical risk model with an easily calculable clinical risk score (CRS) to predict mortality and major morbidity after isolated TV surgery. Multi-state Society of Thoracic Surgeons database records were evaluated for 2,050 isolated TV repair and replacement operations for any etiology performed at 50 hospitals (2002-2014). Parsimonious preoperative risk prediction models were developed using multi-level mixed effects regression to estimate mortality and composite major morbidity risk. Model results were utilized to establish a novel CRS for patients undergoing TV operations. Models were evaluated for discrimination and calibration. Operative mortality and composite major morbidity rates were 9% and 42%, respectively. Final regression models performed well (both P<0.001, AUC = 0.74 and 0.76) and included preoperative factors: age, gender, stroke, hemodialysis, ejection fraction, lung disease, NYHA class, reoperation and urgent or emergency status (all P<0.05). A simple CRS from 0-10+ was highly associated (P<0.001) with incremental increases in predicted mortality and major morbidity. Predicted mortality risk ranged from 2%-34% across CRS categories, while predicted major morbidity risk ranged from 13%-71%. Mortality and major morbidity after isolated TV surgery can be predicted using preoperative patient data from the STS Adult Cardiac Database. A simple clinical risk score predicts mortality and major morbidity after isolated TV surgery. This score may facilitate perioperative counseling and identification of suitable patients for TV surgery. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Development and validation of a Markov microsimulation model for the economic evaluation of treatments in osteoporosis.

PubMed

Hiligsmann, Mickaël; Ethgen, Olivier; Bruyère, Olivier; Richy, Florent; Gathon, Henry-Jean; Reginster, Jean-Yves

2009-01-01

Markov models are increasingly used in economic evaluations of treatments for osteoporosis. Most of the existing evaluations are cohort-based Markov models missing comprehensive memory management and versatility. In this article, we describe and validate an original Markov microsimulation model to accurately assess the cost-effectiveness of prevention and treatment of osteoporosis. We developed a Markov microsimulation model with a lifetime horizon and a direct health-care cost perspective. The patient history was recorded and was used in calculations of transition probabilities, utilities, and costs. To test the internal consistency of the model, we carried out an example calculation for alendronate therapy. Then, external consistency was investigated by comparing absolute lifetime risk of fracture estimates with epidemiologic data. For women at age 70 years, with a twofold increase in the fracture risk of the average population, the costs per quality-adjusted life-year gained for alendronate therapy versus no treatment were estimated at €9105 and €15,325, respectively, under full and realistic adherence assumptions. All the sensitivity analyses in terms of model parameters and modeling assumptions were coherent with expected conclusions and absolute lifetime risk of fracture estimates were within the range of previous estimates, which confirmed both internal and external consistency of the model. Microsimulation models present some major advantages over cohort-based models, increasing the reliability of the results and being largely compatible with the existing state of the art, evidence-based literature. The developed model appears to be a valid model for use in economic evaluations in osteoporosis.

Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom.

PubMed

Harrison, David A; Brady, Anthony R; Parry, Gareth J; Carpenter, James R; Rowan, Kathy

2006-05-01

To assess the performance of published risk prediction models in common use in adult critical care in the United Kingdom and to recalibrate these models in a large representative database of critical care admissions. Prospective cohort study. A total of 163 adult general critical care units in England, Wales, and Northern Ireland, during the period of December 1995 to August 2003. A total of 231,930 admissions, of which 141,106 met inclusion criteria and had sufficient data recorded for all risk prediction models. None. The published versions of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE II UK, APACHE III, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Models (MPM) II were evaluated for discrimination and calibration by means of a combination of appropriate statistical measures recommended by an expert steering committee. All models showed good discrimination (the c index varied from 0.803 to 0.832) but imperfect calibration. Recalibration of the models, which was performed by both the Cox method and re-estimating coefficients, led to improved discrimination and calibration, although all models still showed significant departures from perfect calibration. Risk prediction models developed in another country require validation and recalibration before being used to provide risk-adjusted outcomes within a new country setting. Periodic reassessment is beneficial to ensure calibration is maintained.

Methods for assessing fracture risk prediction models: experience with FRAX in a large integrated health care delivery system.

PubMed

Pressman, Alice R; Lo, Joan C; Chandra, Malini; Ettinger, Bruce

2011-01-01

Area under the receiver operating characteristics (AUROC) curve is often used to evaluate risk models. However, reclassification tests provide an alternative assessment of model performance. We performed both evaluations on results from FRAX (World Health Organization Collaborating Centre for Metabolic Bone Diseases, University of Sheffield, UK), a fracture risk tool, using Kaiser Permanente Northern California women older than 50yr with bone mineral density (BMD) measured during 1997-2003. We compared FRAX performance with and without BMD in the model. Among 94,489 women with mean follow-up of 6.6yr, 1579 (1.7%) sustained a hip fracture. Overall, AUROCs were 0.83 and 0.84 for FRAX without and with BMD, suggesting that BMD did not contribute to model performance. AUROC decreased with increasing age, and BMD contributed significantly to higher AUROC among those aged 70yr and older. Using an 81% sensitivity threshold (optimum level from receiver operating characteristic curve, corresponding to 1.2% cutoff), 35% of those categorized above were reassigned below when BMD was added. In contrast, only 10% of those categorized below were reassigned to the higher risk category when BMD was added. The net reclassification improvement was 5.5% (p<0.01). Two versions of this risk tool have similar AUROCs, but alternative assessments indicate that addition of BMD improves performance. Multiple methods should be used to evaluate risk tool performance with less reliance on AUROC alone. Copyright © 2011 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

The multiple stressor ecological risk assessment for the mercury-contaminated South River and upper Shenandoah River using the Bayesian network-relative risk model.

PubMed

Landis, Wayne G; Ayre, Kimberley K; Johns, Annie F; Summers, Heather M; Stinson, Jonah; Harris, Meagan J; Herring, Carlie E; Markiewicz, April J

2017-01-01

We have conducted a regional scale risk assessment using the Bayesian Network Relative Risk Model (BN-RRM) to calculate the ecological risks to the South River and upper Shenandoah River study area. Four biological endpoints (smallmouth bass, white sucker, Belted Kingfisher, and Carolina Wren) and 4 abiotic endpoints (Fishing River Use, Swimming River Use, Boating River Use, and Water Quality Standards) were included in this risk assessment, based on stakeholder input. Although mercury (Hg) contamination was the original impetus for the site being remediated, other chemical and physical stressors were evaluated. There were 3 primary conclusions from the BN-RRM results. First, risk varies according to location, type and quality of habitat, and exposure to stressors within the landscape. The patterns of risk can be evaluated with reasonable certitude. Second, overall risk to abiotic endpoints was greater than overall risk to biotic endpoints. By including both biotic and abiotic endpoints, we are able to compare risk to endpoints that represent a wide range of stakeholder values. Third, whereas Hg reduction is the regulatory priority for the South River, Hg is not the only stressor driving risk to the endpoints. Ecological and habitat stressors contribute risk to the endpoints and should be considered when managing this site. This research provides the foundation for evaluating the risks of multiple stressors of the South River to a variety of endpoints. From this foundation, tools for the evaluation of management options and an adaptive management tools have been forged. Integr Environ Assess Manag 2017;13:85-99. © 2016 SETAC. © 2016 SETAC.

Investment risk evaluation and countermeasures suggestions of Power Grid Corp under the background of electric power reform

NASA Astrophysics Data System (ADS)

Yang, Chunhui; Su, Zhixiong; Wang, Yuqing; Liu, Yiqun; Qi, Yongwei

2017-03-01

Investment management is an important part of Power Grid Corp. The new electricity reform put forward the general idea of "three release, three strengthening, one independence", which brings new risks to the investment management of the Power Grid Corp. First, the paper analyzes the new risks faced by the Power Grid Corp investment under the background of the electricity reform. Second, the AHP-Fuzzy evaluation model of investment risk of Power Grid Corp is established, and taking Shenzhen Power Supply Bureau as an example, the paper evaluated its risk level of investment plan in 2017. Finally, in the context of the electricity reform, the strategy of the Power Grid Corp's investment risk is proposed.

Methodology for Developing a Probabilistic Risk Assessment Model of Spacecraft Rendezvous and Dockings

NASA Technical Reports Server (NTRS)

Farnham, Steven J., II; Garza, Joel, Jr.; Castillo, Theresa M.; Lutomski, Michael

2011-01-01

In 2007 NASA was preparing to send two new visiting vehicles carrying logistics and propellant to the International Space Station (ISS). These new vehicles were the European Space Agency s (ESA) Automated Transfer Vehicle (ATV), the Jules Verne, and the Japanese Aerospace and Explorations Agency s (JAXA) H-II Transfer Vehicle (HTV). The ISS Program wanted to quantify the increased risk to the ISS from these visiting vehicles. At the time, only the Shuttle, the Soyuz, and the Progress vehicles rendezvoused and docked to the ISS. The increased risk to the ISS was from an increase in vehicle traffic, thereby, increasing the potential catastrophic collision during the rendezvous and the docking or berthing of the spacecraft to the ISS. A universal method of evaluating the risk of rendezvous and docking or berthing was created by the ISS s Risk Team to accommodate the increasing number of rendezvous and docking or berthing operations due to the increasing number of different spacecraft, as well as the future arrival of commercial spacecraft. Before the first docking attempt of ESA's ATV and JAXA's HTV to the ISS, a probabilistic risk model was developed to quantitatively calculate the risk of collision of each spacecraft with the ISS. The 5 rendezvous and docking risk models (Soyuz, Progress, Shuttle, ATV, and HTV) have been used to build and refine the modeling methodology for rendezvous and docking of spacecrafts. This risk modeling methodology will be NASA s basis for evaluating the addition of future ISS visiting spacecrafts hazards, including SpaceX s Dragon, Orbital Science s Cygnus, and NASA s own Orion spacecraft. This paper will describe the methodology used for developing a visiting vehicle risk model.

Study on quantitative risk assessment model of the third party damage for natural gas pipelines based on fuzzy comprehensive assessment

NASA Astrophysics Data System (ADS)

Qiu, Zeyang; Liang, Wei; Wang, Xue; Lin, Yang; Zhang, Meng

2017-05-01

As an important part of national energy supply system, transmission pipelines for natural gas are possible to cause serious environmental pollution, life and property loss in case of accident. The third party damage is one of the most significant causes for natural gas pipeline system accidents, and it is very important to establish an effective quantitative risk assessment model of the third party damage for reducing the number of gas pipelines operation accidents. Against the third party damage accident has the characteristics such as diversity, complexity and uncertainty, this paper establishes a quantitative risk assessment model of the third party damage based on Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE). Firstly, risk sources of third party damage should be identified exactly, and the weight of factors could be determined via improved AHP, finally the importance of each factor is calculated by fuzzy comprehensive evaluation model. The results show that the quantitative risk assessment model is suitable for the third party damage of natural gas pipelines and improvement measures could be put forward to avoid accidents based on the importance of each factor.

Validation of Risk Assessment Models of Venous Thromboembolism in Hospitalized Medical Patients.

PubMed

Greene, M Todd; Spyropoulos, Alex C; Chopra, Vineet; Grant, Paul J; Kaatz, Scott; Bernstein, Steven J; Flanders, Scott A

2016-09-01

Patients hospitalized for acute medical illness are at increased risk for venous thromboembolism. Although risk assessment is recommended and several at-admission risk assessment models have been developed, these have not been adequately derived or externally validated. Therefore, an optimal approach to evaluate venous thromboembolism risk in medical patients is not known. We conducted an external validation study of existing venous thromboembolism risk assessment models using data collected on 63,548 hospitalized medical patients as part of the Michigan Hospital Medicine Safety (HMS) Consortium. For each patient, cumulative venous thromboembolism risk scores and risk categories were calculated. Cox regression models were used to quantify the association between venous thromboembolism events and assigned risk categories. Model discrimination was assessed using Harrell's C-index. Venous thromboembolism incidence in hospitalized medical patients is low (1%). Although existing risk assessment models demonstrate good calibration (hazard ratios for "at-risk" range 2.97-3.59), model discrimination is generally poor for all risk assessment models (C-index range 0.58-0.64). The performance of several existing risk assessment models for predicting venous thromboembolism among acutely ill, hospitalized medical patients at admission is limited. Given the low venous thromboembolism incidence in this nonsurgical patient population, careful consideration of how best to utilize existing venous thromboembolism risk assessment models is necessary, and further development and validation of novel venous thromboembolism risk assessment models for this patient population may be warranted. Published by Elsevier Inc.

Plasma Free Amino Acid Profiles Predict Four-Year Risk of Developing Diabetes, Metabolic Syndrome, Dyslipidemia, and Hypertension in Japanese Population

PubMed Central

Yamakado, Minoru; Nagao, Kenji; Imaizumi, Akira; Tani, Mizuki; Toda, Akiko; Tanaka, Takayuki; Jinzu, Hiroko; Miyano, Hiroshi; Yamamoto, Hiroshi; Daimon, Takashi; Horimoto, Katsuhisa; Ishizaka, Yuko

2015-01-01

Plasma free amino acid (PFAA) profile is highlighted in its association with visceral obesity and hyperinsulinemia, and future diabetes. Indeed PFAA profiling potentially can evaluate individuals’ future risks of developing lifestyle-related diseases, in addition to diabetes. However, few studies have been performed especially in Asian populations, about the optimal combination of PFAAs for evaluating health risks. We quantified PFAA levels in 3,701 Japanese subjects, and determined visceral fat area (VFA) and two-hour post-challenge insulin (Ins120 min) values in 865 and 1,160 subjects, respectively. Then, models between PFAA levels and the VFA or Ins120 min values were constructed by multiple linear regression analysis with variable selection. Finally, a cohort study of 2,984 subjects to examine capabilities of the obtained models for predicting four-year risk of developing new-onset lifestyle-related diseases was conducted. The correlation coefficients of the obtained PFAA models against VFA or Ins120 min were higher than single PFAA level. Our models work well for future risk prediction. Even after adjusting for commonly accepted multiple risk factors, these models can predict future development of diabetes, metabolic syndrome, and dyslipidemia. PFAA profiles confer independent and differing contributions to increasing the lifestyle-related disease risks in addition to the currently known factors in a general Japanese population. PMID:26156880

Determining an appropriate integrated assessment model of tourism safety risk within the Changbai Mountain Scenic Area

NASA Astrophysics Data System (ADS)

Zhou, Lijun; Liu, Jisheng

2017-03-01

Tourism safety is gradually gaining more attention due to the rapid development of the tourism industry in China. Changbai Mountain is one of the most famous mountainous scenic areas in Northeast Asia. Assessment on Changbai Mountain scenic area’s tourism safety risk could do a favor in detecting influence factor of tourism safety risk and classifying tourism safety risk rank, thereby reducing and preventing associated tourism safety risks. This paper uses the Changbai Mountain scenic area as the study subject. By the means of experts scoring and analytic hierarchy process on quantified relevant evaluation indicator, the grid GIS method is used to vectorize the relevant data within a 1000m grid. It respectively analyzes main indicators associated tourism safety risk in Changbai Mountain scenic area, including hazard, exposure, vulnerability and ability to prevent and mitigate disasters. The integrated tourism safety risk model is used to comprehensively evaluate tourism safety risk in Changbai Mountain scenic area.

Using toxicokinetic-toxicodynamic modeling as an acute risk assessment refinement approach in vertebrate ecological risk assessment.

PubMed

Ducrot, Virginie; Ashauer, Roman; Bednarska, Agnieszka J; Hinarejos, Silvia; Thorbek, Pernille; Weyman, Gabriel

2016-01-01

Recent guidance identified toxicokinetic-toxicodynamic (TK-TD) modeling as a relevant approach for risk assessment refinement. Yet, its added value compared to other refinement options is not detailed, and how to conduct the modeling appropriately is not explained. This case study addresses these issues through 2 examples of individual-level risk assessment for 2 hypothetical plant protection products: 1) evaluating the risk for small granivorous birds and small omnivorous mammals of a single application, as a seed treatment in winter cereals, and 2) evaluating the risk for fish after a pulsed treatment in the edge-of-field zone. Using acute test data, we conducted the first tier risk assessment as defined in the European Food Safety Authority (EFSA) guidance. When first tier risk assessment highlighted a concern, refinement options were discussed. Cases where the use of models should be preferred over other existing refinement approaches were highlighted. We then practically conducted the risk assessment refinement by using 2 different models as examples. In example 1, a TK model accounting for toxicokinetics and relevant feeding patterns in the skylark and in the wood mouse was used to predict internal doses of the hypothetical active ingredient in individuals, based on relevant feeding patterns in an in-crop situation, and identify the residue levels leading to mortality. In example 2, a TK-TD model accounting for toxicokinetics, toxicodynamics, and relevant exposure patterns in the fathead minnow was used to predict the time-course of fish survival for relevant FOCUS SW exposure scenarios and identify which scenarios might lead to mortality. Models were calibrated using available standard data and implemented to simulate the time-course of internal dose of active ingredient or survival for different exposure scenarios. Simulation results were discussed and used to derive the risk assessment refinement endpoints used for decision. Finally, we compared the "classical" risk assessment approach with the model-based approach. These comparisons showed that TK and TK-TD models can bring more realism to the risk assessment through the possibility to study realistic exposure scenarios and to simulate relevant mechanisms of effects (including delayed toxicity and recovery). Noticeably, using TK-TD models is currently the most relevant way to directly connect realistic exposure patterns to effects. We conclude with recommendations on how to properly use TK and TK-TD model in acute risk assessment for vertebrates. © 2015 SETAC.

Software reliability through fault-avoidance and fault-tolerance

NASA Technical Reports Server (NTRS)

Vouk, Mladen A.; Mcallister, David F.

1992-01-01

Accomplishments in the following research areas are summarized: structure based testing, reliability growth, and design testability with risk evaluation; reliability growth models and software risk management; and evaluation of consensus voting, consensus recovery block, and acceptance voting. Four papers generated during the reporting period are included as appendices.

AHP for Risk Management Based on Expected Utility Theory

NASA Astrophysics Data System (ADS)

Azuma, Rumiko; Miyagi, Hayao

This paper presents a model of decision-making considering the risk assessment. The conventional evaluation in AHP is considered to be a kind of utility. When dealing with the risk, however, it is necessary to consider the probability of damage. In order to take risk into decision-making problem, we construct AHP based on expected utility. The risk is considered as a related element of criterion rather than criterion itself. The expected utility is integrated, considering that satisfaction is positive utility and damage by risk is negative utility. Then, evaluation in AHP is executed using the expected utility.

A framework for quantifying net benefits of alternative prognostic models‡

PubMed Central

Rapsomaniki, Eleni; White, Ian R; Wood, Angela M; Thompson, Simon G

2012-01-01

New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21905066

Ecosystem Risk Assessment Using the Comprehensive Assessment of Risk to Ecosystems (CARE) Tool

NASA Astrophysics Data System (ADS)

Battista, W.; Fujita, R.; Karr, K.

2016-12-01

Effective Ecosystem Based Management requires a localized understanding of the health and functioning of a given system as well as of the various factors that may threaten the ongoing ability of the system to support the provision of valued services. Several risk assessment models are available that can provide a scientific basis for understanding these factors and for guiding management action, but these models focus mainly on single species and evaluate only the impacts of fishing in detail. We have developed a new ecosystem risk assessment model - the Comprehensive Assessment of Risk to Ecosystems (CARE) - that allows analysts to consider the cumulative impact of multiple threats, interactions among multiple threats that may result in synergistic or antagonistic impacts, and the impacts of a suite of threats on whole-ecosystem productivity and functioning, as well as on specific ecosystem services. The CARE model was designed to be completed in as little as two hours, and uses local and expert knowledge where data are lacking. The CARE tool can be used to evaluate risks facing a single site; to compare multiple sites for the suitability or necessity of different management options; or to evaluate the effects of a proposed management action aimed at reducing one or more risks. This analysis can help users identify which threats are the most important at a given site, and therefore where limited management resources should be targeted. CARE can be applied to virtually any system, and can be modified as knowledge is gained or to better match different site characteristics. CARE builds on previous ecosystem risk assessment tools to provide a comprehensive assessment of fishing and non-fishing threats that can be used to inform environmental management decisions across a broad range of systems.

Ecosystem Risk Assessment Using the Comprehensive Assessment of Risk to Ecosystems (CARE) Tool

NASA Astrophysics Data System (ADS)

Battista, W.; Fujita, R.; Karr, K.

2016-02-01

Effective Ecosystem Based Management requires a localized understanding of the health and functioning of a given system as well as of the various factors that may threaten the ongoing ability of the system to support the provision of valued services. Several risk assessment models are available that can provide a scientific basis for understanding these factors and for guiding management action, but these models focus mainly on single species and evaluate only the impacts of fishing in detail. We have developed a new ecosystem risk assessment model - the Comprehensive Assessment of Risk to Ecosystems (CARE) - that allows analysts to consider the cumulative impact of multiple threats, interactions among multiple threats that may result in synergistic or antagonistic impacts, and the impacts of a suite of threats on whole-ecosystem productivity and functioning, as well as on specific ecosystem services. The CARE model was designed to be completed in as little as two hours, and uses local and expert knowledge where data are lacking. The CARE tool can be used to evaluate risks facing a single site; to compare multiple sites for the suitability or necessity of different management options; or to evaluate the effects of a proposed management action aimed at reducing one or more risks. This analysis can help users identify which threats are the most important at a given site, and therefore where limited management resources should be targeted. CARE can be applied to virtually any system, and can be modified as knowledge is gained or to better match different site characteristics. CARE builds on previous ecosystem risk assessment tools to provide a comprehensive assessment of fishing and non-fishing threats that can be used to inform environmental management decisions across a broad range of systems.

Modeling hard clinical end-point data in economic analyses.

PubMed

Kansal, Anuraag R; Zheng, Ying; Palencia, Roberto; Ruffolo, Antonio; Hass, Bastian; Sorensen, Sonja V

2013-11-01

The availability of hard clinical end-point data, such as that on cardiovascular (CV) events among patients with type 2 diabetes mellitus, is increasing, and as a result there is growing interest in using hard end-point data of this type in economic analyses. This study investigated published approaches for modeling hard end-points from clinical trials and evaluated their applicability in health economic models with different disease features. A review of cost-effectiveness models of interventions in clinically significant therapeutic areas (CV diseases, cancer, and chronic lower respiratory diseases) was conducted in PubMed and Embase using a defined search strategy. Only studies integrating hard end-point data from randomized clinical trials were considered. For each study included, clinical input characteristics and modeling approach were summarized and evaluated. A total of 33 articles (23 CV, eight cancer, two respiratory) were accepted for detailed analysis. Decision trees, Markov models, discrete event simulations, and hybrids were used. Event rates were incorporated either as constant rates, time-dependent risks, or risk equations based on patient characteristics. Risks dependent on time and/or patient characteristics were used where major event rates were >1%/year in models with fewer health states (<7). Models of infrequent events or with numerous health states generally preferred constant event rates. The detailed modeling information and terminology varied, sometimes requiring interpretation. Key considerations for cost-effectiveness models incorporating hard end-point data include the frequency and characteristics of the relevant clinical events and how the trial data is reported. When event risk is low, simplification of both the model structure and event rate modeling is recommended. When event risk is common, such as in high risk populations, more detailed modeling approaches, including individual simulations or explicitly time-dependent event rates, are more appropriate to accurately reflect the trial data.

Risk-adjusted Outcomes of Clinically Relevant Pancreatic Fistula Following Pancreatoduodenectomy: A Model for Performance Evaluation.

PubMed

McMillan, Matthew T; Soi, Sameer; Asbun, Horacio J; Ball, Chad G; Bassi, Claudio; Beane, Joal D; Behrman, Stephen W; Berger, Adam C; Bloomston, Mark; Callery, Mark P; Christein, John D; Dixon, Elijah; Drebin, Jeffrey A; Castillo, Carlos Fernandez-Del; Fisher, William E; Fong, Zhi Ven; House, Michael G; Hughes, Steven J; Kent, Tara S; Kunstman, John W; Malleo, Giuseppe; Miller, Benjamin C; Salem, Ronald R; Soares, Kevin; Valero, Vicente; Wolfgang, Christopher L; Vollmer, Charles M

2016-08-01

To evaluate surgical performance in pancreatoduodenectomy using clinically relevant postoperative pancreatic fistula (CR-POPF) occurrence as a quality indicator. Accurate assessment of surgeon and institutional performance requires (1) standardized definitions for the outcome of interest and (2) a comprehensive risk-adjustment process to control for differences in patient risk. This multinational, retrospective study of 4301 pancreatoduodenectomies involved 55 surgeons at 15 institutions. Risk for CR-POPF was assessed using the previously validated Fistula Risk Score, and pancreatic fistulas were stratified by International Study Group criteria. CR-POPF variability was evaluated and hierarchical regression analysis assessed individual surgeon and institutional performance. There was considerable variability in both CR-POPF risk and occurrence. Factors increasing the risk for CR-POPF development included increasing Fistula Risk Score (odds ratio 1.49 per point, P < 0.00001) and octreotide (odds ratio 3.30, P < 0.00001). When adjusting for risk, performance outliers were identified at the surgeon and institutional levels. Of the top 10 surgeons (≥15 cases) for nonrisk-adjusted performance, only 6 remained in this high-performing category following risk adjustment. This analysis of pancreatic fistulas following pancreatoduodenectomy demonstrates considerable variability in both the risk and occurrence of CR-POPF among surgeons and institutions. Disparities in patient risk between providers reinforce the need for comprehensive, risk-adjusted modeling when assessing performance based on procedure-specific complications. Furthermore, beyond inherent patient risk factors, surgical decision-making influences fistula outcomes.

Food-chain contamination evaluations in ecological risk assessments

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

Linder, G.

Food-chain models have become increasingly important within the ecological risk assessment process. This is the case particularly when acute effects are not readily apparent, or the contaminants of concern are not readily detoxified, have a high likelihood for partitioning into lipids, or have specific target organs or tissues that may increase their significance in evaluating their potential adverse effects. An overview of food-chain models -- conceptual, theoretical, and empirical -- will be considered through a series of papers that will focus on their application within the ecological risk assessment process. Whether a food-chain evaluation is being developed to address relativelymore » simple questions related to chronic effects of toxicants on target populations, or whether a more complex food-web model is being developed to address questions related to multiple-trophic level transfers of toxicants, the elements within the food chain contamination evaluation can be generalized to address the mechanisms of toxicant accumulation in individual organisms. This can then be incorporated into more elaborate models that consider these organismal-level processes within the context of a species life-history or community-level responses that may be associated with long-term exposures.« less

Evaluation of Foreign Investment in Power Plants using Real Options

NASA Astrophysics Data System (ADS)

Kato, Moritoshi; Zhou, Yicheng

This paper proposes new methods for evaluating foreign investment in power plants under market uncertainty using a real options approach. We suppose a thermal power plant project in a deregulated electricity market. One of our proposed methods is that we calculate the cash flow generated by the project in a reference year using actual market data to incorporate periodic characteristics of energy prices into a yearly cash flow model. We make the stochastic yearly cash flow model with the initial value which is the cash flow in the reference year, and certain trend and volatility. Then we calculate the real options value (ROV) of the project which has abandonment options using the yearly cash flow model. Another our proposed method is that we evaluate foreign currency/domestic currency exchange rate risk by representing ROV in foreign currency as yearly pay off and exchanging it to ROV in domestic currency using a stochastic exchange rate model. We analyze the effect of the heat rate and operation and maintenance costs of the power plant on ROV, and evaluate exchange rate risk through numerical examples. Our proposed method will be useful for the risk management of foreign investment in power plants.

Risk evaluation on leading companies in property and real estate subsector at IDX: A Value-at-Risk with ARMAX-GARCHX approach and duration test

NASA Astrophysics Data System (ADS)

Dwi Prastyo, Dedy; Handayani, Dwi; Fam, Soo-Fen; Puteri Rahayu, Santi; Suhartono; Luh Putu Satyaning Pradnya Paramita, Ni

2018-03-01

Risk assessment and evaluation becomes essential for financial institution to measure the potential risk of their counterparties. In middle of 2016 until first quarter of 2017, there is national program from Indonesian government so-called Tax Amnesty. One subsector that has potential to receive positive impact from the Tax Amnesty program is property and real estate. This work evaluates the risk of top five companies in term of capital share listed in Indonesia stock exchange (IDX). To do this, the Value-at-Risk (VaR) with ARMAX-GARCHX approach is employed. The ARMAX-GARCHX simultaneously models the adaptive mean and variance of stock return of each company considering exogenous variables, i.e. IDR/USD exchange rate and Jakarta Composite Index (JCI). The risk is evaluated in scheme of time moving window. The risk evaluation using 5% quantile with window size 500 transaction days perform better result compare to other scenarios. In addition, duration test is used to test the dependency between shortfalls. It informs that series of shortfall are independent.

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).

Environmental risk assessment of selected organic chemicals based on TOC test and QSAR estimation models.

PubMed

Chi, Yulang; Zhang, Huanteng; Huang, Qiansheng; Lin, Yi; Ye, Guozhu; Zhu, Huimin; Dong, Sijun

2018-02-01

Environmental risks of organic chemicals have been greatly determined by their persistence, bioaccumulation, and toxicity (PBT) and physicochemical properties. Major regulations in different countries and regions identify chemicals according to their bioconcentration factor (BCF) and octanol-water partition coefficient (Kow), which frequently displays a substantial correlation with the sediment sorption coefficient (Koc). Half-life or degradability is crucial for the persistence evaluation of chemicals. Quantitative structure activity relationship (QSAR) estimation models are indispensable for predicting environmental fate and health effects in the absence of field- or laboratory-based data. In this study, 39 chemicals of high concern were chosen for half-life testing based on total organic carbon (TOC) degradation, and two widely accepted and highly used QSAR estimation models (i.e., EPI Suite and PBT Profiler) were adopted for environmental risk evaluation. The experimental results and estimated data, as well as the two model-based results were compared, based on the water solubility, Kow, Koc, BCF and half-life. Environmental risk assessment of the selected compounds was achieved by combining experimental data and estimation models. It was concluded that both EPI Suite and PBT Profiler were fairly accurate in measuring the physicochemical properties and degradation half-lives for water, soil, and sediment. However, the half-lives between the experimental and the estimated results were still not absolutely consistent. This suggests deficiencies of the prediction models in some ways, and the necessity to combine the experimental data and predicted results for the evaluation of environmental fate and risks of pollutants. Copyright © 2016. Published by Elsevier B.V.

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

EPA Science Inventory

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

Risk maps for navigation in liver surgery

NASA Astrophysics Data System (ADS)

Hansen, C.; Zidowitz, S.; Schenk, A.; Oldhafer, K.-J.; Lang, H.; Peitgen, H.-O.

2010-02-01

The optimal transfer of preoperative planning data and risk evaluations to the operative site is challenging. A common practice is to use preoperative 3D planning models as a printout or as a presentation on a display. One important aspect is that these models were not developed to provide information in complex workspaces like the operating room. Our aim is to reduce the visual complexity of 3D planning models by mapping surgically relevant information onto a risk map. Therefore, we present methods for the identification and classification of critical anatomical structures in the proximity of a preoperatively planned resection surface. Shadow-like distance indicators are introduced to encode the distance from the resection surface to these critical structures on the risk map. In addition, contour lines are used to accentuate shape and spatial depth. The resulting visualization is clear and intuitive, allowing for a fast mental mapping of the current resection surface to the risk map. Preliminary evaluations by liver surgeons indicate that damage to risk structures may be prevented and patient safety may be enhanced using the proposed methods.

Evaluation of potential risks from ash disposal site leachate

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

Mills, W.B.; Loh, J.Y.; Bate, M.C.

1999-04-01

A risk-based approach is used to evaluate potential human health risks associated with a discharge from an ash disposal site into a small stream. The RIVRISK model was used to estimate downstream concentrations and corresponding risks. The modeling and risk analyses focus on boron, the constituent of greatest potential concern to public health at the site investigated, in Riddle Run, Pennsylvania. Prior to performing the risk assessment, the model is validated by comparing observed and predicted results. The comparison is good and an uncertainty analysis is provided to explain the comparison. The hazard quotient (HQ) for boron is predicted tomore » be greater than 1 at presently regulated compliance points over a range of flow rates. The reference dose (RfD) currently recommended by the United States Environmental Protection Agency (US EPA) was used for the analyses. However, the toxicity of boron as expressed by the RfD is now under review by both the U.S. EPA and the World Health Organization. Alternative reference doses being examined would produce predicted boron hazard quotients of less than 1 at nearly all flow conditions.« less

Combined impact of lead, cadmium, polychlorinated biphenyls and non-chemical risk factors on blood pressure in NHANES

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

Peters, Junenette L., E-mail: petersj@bu.edu; Patricia Fabian, M., E-mail: pfabian@bu.edu; Levy, Jonathan I., E-mail: jonlevy@bu.edu

High blood pressure is associated with exposure to multiple chemical and non-chemical risk factors, but epidemiological analyses to date have not assessed the combined effects of both chemical and non-chemical stressors on human populations in the context of cumulative risk assessment. We developed a novel modeling approach to evaluate the combined impact of lead, cadmium, polychlorinated biphenyls (PCBs), and multiple non-chemical risk factors on four blood pressure measures using data for adults aged ≥20 years from the National Health and Nutrition Examination Survey (1999–2008). We developed predictive models for chemical and other stressors. Structural equation models were applied to accountmore » for complex associations among predictors of stressors as well as blood pressure. Models showed that blood lead, serum PCBs, and established non-chemical stressors were significantly associated with blood pressure. Lead was the chemical stressor most predictive of diastolic blood pressure and mean arterial pressure, while PCBs had a greater influence on systolic blood pressure and pulse pressure, and blood cadmium was not a significant predictor of blood pressure. The simultaneously fit exposure models explained 34%, 43% and 52% of the variance for lead, cadmium and PCBs, respectively. The structural equation models were developed using predictors available from public data streams (e.g., U.S. Census), which would allow the models to be applied to any U.S. population exposed to these multiple stressors in order to identify high risk subpopulations, direct intervention strategies, and inform public policy. - Highlights: • We evaluated joint impact of chemical and non-chemical stressors on blood pressure. • We built predictive models for lead, cadmium and polychlorinated biphenyls (PCBs). • Our approach allows joint evaluation of predictors from population-specific data. • Lead, PCBs and established non-chemical stressors were related to blood pressure. • Framework allows cumulative risk assessment in specific geographic settings.« less

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

PubMed

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

2014-05-01

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

Mathematical model to estimate risk of calcium-containing renal stones

NASA Technical Reports Server (NTRS)

Pietrzyk, R. A.; Feiveson, A. H.; Whitson, P. A.

1999-01-01

BACKGROUND/AIMS: Astronauts exposed to microgravity during the course of spaceflight undergo physiologic changes that alter the urinary environment so as to increase the risk of renal stone formation. This study was undertaken to identify a simple method with which to evaluate the potential risk of renal stone development during spaceflight. METHOD: We used a large database of urinary risk factors obtained from 323 astronauts before and after spaceflight to generate a mathematical model with which to predict the urinary supersaturation of calcium stone forming salts. RESULT: This model, which involves the fewest possible analytical variables (urinary calcium, citrate, oxalate, phosphorus, and total volume), reliably and accurately predicted the urinary supersaturation of the calcium stone forming salts when compared to results obtained from a group of 6 astronauts who collected urine during flight. CONCLUSIONS: The use of this model will simplify both routine medical monitoring during spaceflight as well as the evaluation of countermeasures designed to minimize renal stone development. This model also can be used for Earth-based applications in which access to analytical resources is limited.

Risk assessment of flood disaster and forewarning model at different spatial-temporal scales

NASA Astrophysics Data System (ADS)

Zhao, Jun; Jin, Juliang; Xu, Jinchao; Guo, Qizhong; Hang, Qingfeng; Chen, Yaqian

2018-05-01

Aiming at reducing losses from flood disaster, risk assessment of flood disaster and forewarning model is studied. The model is built upon risk indices in flood disaster system, proceeding from the whole structure and its parts at different spatial-temporal scales. In this study, on the one hand, it mainly establishes the long-term forewarning model for the surface area with three levels of prediction, evaluation, and forewarning. The method of structure-adaptive back-propagation neural network on peak identification is used to simulate indices in prediction sub-model. Set pair analysis is employed to calculate the connection degrees of a single index, comprehensive index, and systematic risk through the multivariate connection number, and the comprehensive assessment is made by assessment matrixes in evaluation sub-model. The comparison judging method is adopted to divide warning degree of flood disaster on risk assessment comprehensive index with forewarning standards in forewarning sub-model and then the long-term local conditions for proposing planning schemes. On the other hand, it mainly sets up the real-time forewarning model for the spot, which introduces the real-time correction technique of Kalman filter based on hydrological model with forewarning index, and then the real-time local conditions for presenting an emergency plan. This study takes Tunxi area, Huangshan City of China, as an example. After risk assessment and forewarning model establishment and application for flood disaster at different spatial-temporal scales between the actual and simulated data from 1989 to 2008, forewarning results show that the development trend for flood disaster risk remains a decline on the whole from 2009 to 2013, despite the rise in 2011. At the macroscopic level, project and non-project measures are advanced, while at the microcosmic level, the time, place, and method are listed. It suggests that the proposed model is feasible with theory and application, thus offering a way for assessing and forewarning flood disaster risk.

Assessment of the disposal of radioactive petroleum industry waste in nonhazardous landfills using risk-based modeling.

PubMed

Smith, Karen P; Arnish, John J; Williams, Gustavious P; Blunt, Deborah L

2003-05-15

Certain petroleum production activities cause naturally occurring radioactive materials (NORM) to accumulate in concentrations above natural background levels, making safe and cost-effective management of such technologically enhanced NORM (TENORM) a key issue for the petroleum industry. As a result, both industry and regulators are interested in identifying cost-effective disposal alternatives that provide adequate protection of human health and the environment One such alternative, currently allowed in Michigan with restrictions, is the disposal of TENORM wastes in nonhazardous waste landfills. The disposal of petroleum industry wastes containing radium-226 (Ra-226) in nonhazardous landfills was modeled to evaluate the potential radiological doses and health risks to workers and the public. Multiple scenarios were considered in evaluating the potential risks associated with landfill operations and the future use of the property. The scenarios were defined, in part, to evaluate the Michigan policy; sensitivity analyses were conducted to evaluate the impact of key parameters on potential risks. The results indicate that the disposal of petroleum industry TENORM wastes in nonhazardous landfills in accordance with the Michigan policy and existing landfill regulations presents a negligible risk to most of the potential receptors considered in this study.

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.

Development and Evaluation of an Automated Machine Learning Algorithm for In-Hospital Mortality Risk Adjustment Among Critical Care Patients.

PubMed

Delahanty, Ryan J; Kaufman, David; Jones, Spencer S

2018-06-01

Risk adjustment algorithms for ICU mortality are necessary for measuring and improving ICU performance. Existing risk adjustment algorithms are not widely adopted. Key barriers to adoption include licensing and implementation costs as well as labor costs associated with human-intensive data collection. Widespread adoption of electronic health records makes automated risk adjustment feasible. Using modern machine learning methods and open source tools, we developed and evaluated a retrospective risk adjustment algorithm for in-hospital mortality among ICU patients. The Risk of Inpatient Death score can be fully automated and is reliant upon data elements that are generated in the course of usual hospital processes. One hundred thirty-one ICUs in 53 hospitals operated by Tenet Healthcare. A cohort of 237,173 ICU patients discharged between January 2014 and December 2016. The data were randomly split into training (36 hospitals), and validation (17 hospitals) data sets. Feature selection and model training were carried out using the training set while the discrimination, calibration, and accuracy of the model were assessed in the validation data set. Model discrimination was evaluated based on the area under receiver operating characteristic curve; accuracy and calibration were assessed via adjusted Brier scores and visual analysis of calibration curves. Seventeen features, including a mix of clinical and administrative data elements, were retained in the final model. The Risk of Inpatient Death score demonstrated excellent discrimination (area under receiver operating characteristic curve = 0.94) and calibration (adjusted Brier score = 52.8%) in the validation dataset; these results compare favorably to the published performance statistics for the most commonly used mortality risk adjustment algorithms. Low adoption of ICU mortality risk adjustment algorithms impedes progress toward increasing the value of the healthcare delivered in ICUs. The Risk of Inpatient Death score has many attractive attributes that address the key barriers to adoption of ICU risk adjustment algorithms and performs comparably to existing human-intensive algorithms. Automated risk adjustment algorithms have the potential to obviate known barriers to adoption such as cost-prohibitive licensing fees and significant direct labor costs. Further evaluation is needed to ensure that the level of performance observed in this study could be achieved at independent sites.

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

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

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

1993-01-01

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

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

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

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

1993-03-01

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

Evaluation of the DAVROS (Development And Validation of Risk-adjusted Outcomes for Systems of emergency care) risk-adjustment model as a quality indicator for healthcare

PubMed Central

Wilson, Richard; Goodacre, Steve W; Klingbajl, Marcin; Kelly, Anne-Maree; Rainer, Tim; Coats, Tim; Holloway, Vikki; Townend, Will; Crane, Steve

2014-01-01

Background and objective Risk-adjusted mortality rates can be used as a quality indicator if it is assumed that the discrepancy between predicted and actual mortality can be attributed to the quality of healthcare (ie, the model has attributional validity). The Development And Validation of Risk-adjusted Outcomes for Systems of emergency care (DAVROS) model predicts 7-day mortality in emergency medical admissions. We aimed to test this assumption by evaluating the attributional validity of the DAVROS risk-adjustment model. Methods We selected cases that had the greatest discrepancy between observed mortality and predicted probability of mortality from seven hospitals involved in validation of the DAVROS risk-adjustment model. Reviewers at each hospital assessed hospital records to determine whether the discrepancy between predicted and actual mortality could be explained by the healthcare provided. Results We received 232/280 (83%) completed review forms relating to 179 unexpected deaths and 53 unexpected survivors. The healthcare system was judged to have potentially contributed to 10/179 (8%) of the unexpected deaths and 26/53 (49%) of the unexpected survivors. Failure of the model to appropriately predict risk was judged to be responsible for 135/179 (75%) of the unexpected deaths and 2/53 (4%) of the unexpected survivors. Some 10/53 (19%) of the unexpected survivors died within a few months of the 7-day period of model prediction. Conclusions We found little evidence that deaths occurring in patients with a low predicted mortality from risk-adjustment could be attributed to the quality of healthcare provided. PMID:23605036

Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation

NASA Technical Reports Server (NTRS)

2012-01-01

At the request of NASA, the National Research Council's (NRC's) Committee for Evaluation of Space Radiation Cancer Risk Model1 reviewed a number of changes that NASA proposes to make to its model for estimating the risk of radiation-induced cancer in astronauts. The NASA model in current use was last updated in 2005, and the proposed model would incorporate recent research directed at improving the quantification and understanding of the health risks posed by the space radiation environment. NASA's proposed model is defined by the 2011 NASA report Space Radiation Cancer Risk Projections and Uncertainties--2010 . The committee's evaluation is based primarily on this source, which is referred to hereafter as the 2011 NASA report, with mention of specific sections or tables. The overall process for estimating cancer risks due to low linear energy transfer (LET) radiation exposure has been fully described in reports by a number of organizations. The approaches described in the reports from all of these expert groups are quite similar. NASA's proposed space radiation cancer risk assessment model calculates, as its main output, age- and gender-specific risk of exposure-induced death (REID) for use in the estimation of mission and astronaut-specific cancer risk. The model also calculates the associated uncertainties in REID. The general approach for estimating risk and uncertainty in the proposed model is broadly similar to that used for the current (2005) NASA model and is based on recommendations by the National Council on Radiation Protection and Measurements. However, NASA's proposed model has significant changes with respect to the following: the integration of new findings and methods into its components by taking into account newer epidemiological data and analyses, new radiobiological data indicating that quality factors differ for leukemia and solid cancers, an improved method for specifying quality factors in terms of radiation track structure concepts as opposed to the previous approach based on linear energy transfer, the development of a new solar particle event (SPE) model, and the updates to galactic cosmic ray (GCR) and shielding transport models. The newer epidemiological information includes updates to the cancer incidence rates from the life span study (LSS) of the Japanese atomic bomb survivors, transferred to the U.S. population and converted to cancer mortality rates from U.S. population statistics. In addition, the proposed model provides an alternative analysis applicable to lifetime never-smokers (NSs). Details of the uncertainty analysis in the model have also been updated and revised. NASA's proposed model and associated uncertainties are complex in their formulation and as such require a very clear and precise set of descriptions. The committee found the 2011 NASA report challenging to review largely because of the lack of clarity in the model descriptions and derivation of the various parameters used. The committee requested some clarifications from NASA throughout its review and was able to resolve many, but not all, of the ambiguities in the written description.

Probabilistic evaluation of damage potential in earthquake-induced liquefaction in a 3-D soil deposit

NASA Astrophysics Data System (ADS)

Halder, A.; Miller, F. J.

1982-03-01

A probabilistic model to evaluate the risk of liquefaction at a site and to limit or eliminate damage during earthquake induced liquefaction is proposed. The model is extended to consider three dimensional nonhomogeneous soil properties. The parameters relevant to the liquefaction phenomenon are identified, including: (1) soil parameters; (2) parameters required to consider laboratory test and sampling effects; and (3) loading parameters. The fundamentals of risk based design concepts pertient to liquefaction are reviewed. A detailed statistical evaluation of the soil parameters in the proposed liquefaction model is provided and the uncertainty associated with the estimation of in situ relative density is evaluated for both direct and indirect methods. It is found that the liquefaction potential the uncertainties in the load parameters could be higher than those in the resistance parameters.

Geographic risk modeling of childhood cancer relative to county-level crops, hazardous air pollutants and population density characteristics in Texas.

PubMed

Thompson, James A; Carozza, Susan E; Zhu, Li

2008-09-25

Childhood cancer has been linked to a variety of environmental factors, including agricultural activities, industrial pollutants and population mixing, but etiologic studies have often been inconclusive or inconsistent when considering specific cancer types. More specific exposure assessments are needed. It would be helpful to optimize future studies to incorporate knowledge of high-risk locations or geographic risk patterns. The objective of this study was to evaluate potential geographic risk patterns in Texas accounting for the possibility that multiple cancers may have similar geographic risks patterns. A spatio-temporal risk modeling approach was used, whereby 19 childhood cancer types were modeled as potentially correlated within county-years. The standard morbidity ratios were modeled as functions of intensive crop production, intensive release of hazardous air pollutants, population density, and rapid population growth. There was supportive evidence for elevated risks for germ cell tumors and "other" gliomas in areas of intense cropping and for hepatic tumors in areas of intense release of hazardous air pollutants. The risk for Hodgkin lymphoma appeared to be reduced in areas of rapidly growing population. Elevated spatial risks included four cancer histotypes, "other" leukemias, Central Nervous System (CNS) embryonal tumors, CNS other gliomas and hepatic tumors with greater than 95% likelihood of elevated risks in at least one county. The Bayesian implementation of the Multivariate Conditional Autoregressive model provided a flexible approach to the spatial modeling of multiple childhood cancer histotypes. The current study identified geographic factors supporting more focused studies of germ cell tumors and "other" gliomas in areas of intense cropping, hepatic cancer near Hazardous Air Pollutant (HAP) release facilities and specific locations with increased risks for CNS embryonal tumors and for "other" leukemias. Further study should be performed to evaluate potentially lower risk for Hodgkin lymphoma and malignant bone tumors in counties with rapidly growing population.

Development and validation of a risk model for identification of non-neutropenic, critically ill adult patients at high risk of invasive Candida infection: the Fungal Infection Risk Evaluation (FIRE) Study.

PubMed

Harrison, D; Muskett, H; Harvey, S; Grieve, R; Shahin, J; Patel, K; Sadique, Z; Allen, E; Dybowski, R; Jit, M; Edgeworth, J; Kibbler, C; Barnes, R; Soni, N; Rowan, K

2013-02-01

There is increasing evidence that invasive fungal disease (IFD) is more likely to occur in non-neutropenic patients in critical care units. A number of randomised controlled trials (RCTs) have evaluated antifungal prophylaxis in non-neutropenic, critically ill patients, demonstrating a reduction in the risk of proven IFD and suggesting a reduction in mortality. It is necessary to establish a method to identify and target antifungal prophylaxis at those patients at highest risk of IFD, who stand to benefit most from any antifungal prophylaxis strategy. To develop and validate risk models to identify non-neutropenic, critically ill adult patients at high risk of invasive Candida infection, who would benefit from antifungal prophylaxis, and to assess the cost-effectiveness of targeting antifungal prophylaxis to high-risk patients based on these models. Systematic review, prospective data collection, statistical modelling, economic decision modelling and value of information analysis. Ninety-six UK adult general critical care units. Consecutive admissions to participating critical care units. None. Invasive fungal disease, defined as a blood culture or sample from a normally sterile site showing yeast/mould cells in a microbiological or histopathological report. For statistical and economic modelling, the primary outcome was invasive Candida infection, defined as IFD-positive for Candida species. Systematic review: Thirteen articles exploring risk factors, risk models or clinical decision rules for IFD in critically ill adult patients were identified. Risk factors reported to be significantly associated with IFD were included in the final data set for the prospective data collection. Data were collected on 60,778 admissions between July 2009 and March 2011. Overall, 383 patients (0.6%) were admitted with or developed IFD. The majority of IFD patients (94%) were positive for Candida species. The most common site of infection was blood (55%). The incidence of IFD identified in unit was 4.7 cases per 1000 admissions, and for unit-acquired IFD was 3.2 cases per 1000 admissions. Statistical modelling: Risk models were developed at admission to the critical care unit, 24 hours and the end of calendar day 3. The risk model at admission had fair discrimination (c-index 0.705). Discrimination improved at 24 hours (c-index 0.823) and this was maintained at the end of calendar day 3 (c-index 0.835). There was a drop in model performance in the validation sample. Economic decision model: Irrespective of risk threshold, incremental quality-adjusted life-years of prophylaxis strategies compared with current practice were positive but small compared with the incremental costs. Incremental net benefits of each prophylaxis strategy compared with current practice were all negative. Cost-effectiveness acceptability curves showed that current practice was the strategy most likely to be cost-effective. Across all parameters in the decision model, results indicated that the value of further research for the whole population of interest might be high relative to the research costs. The results of the Fungal Infection Risk Evaluation (FIRE) Study, derived from a highly representative sample of adult general critical care units across the UK, indicated a low incidence of IFD among non-neutropenic, critically ill adult patients. IFD was associated with substantially higher mortality, more intensive organ support and longer length of stay. Risk modelling produced simple risk models that provided acceptable discrimination for identifying patients at 'high risk' of invasive Candida infection. Results of the economic model suggested that the current most cost-effective treatment strategy for prophylactic use of systemic antifungal agents among non-neutropenic, critically ill adult patients admitted to NHS adult general critical care units is a strategy of no risk assessment and no antifungal prophylaxis. Funding for this study was provided by the Health Technology Assessment programme of the National Institute for Health Research.

DEVELOPMENT AND EVALUATION OF NOVEL DOSE-RESPONSE MODELS FOR USE IN MICROBIAL RISK ASSESSMENT

EPA Science Inventory

This document contains a description of dose-response modeling methods designed to provide a robust approach under uncertainty for predicting human population risk from exposure to pathogens in drinking water.

The purpose of this document is to describe a body of literatu...

A CONCEPTUAL MODEL FOR EVALUATING RELATIVE POTENCY DATA FOR USE IN ECOLOGICAL RISK ASSESSMENTS

EPA Science Inventory

For chemicals with a common mechanism of toxicity, relative potency factors (RPFs) allow dose and exposure measures to be normalized to an equivalent toxicity amount of a model chemical... In ecological risk assessments the large number of possible target species, variety of expo...

An Integrated Approach for Physical and Cyber Security Risk Assessment: The U.S. Army Corps of Engineers Common Risk Model for Dams

DTIC Science & Technology

2016-07-01

Common Risk Model for Dams ( CRM -D) Methodology,” for the Director, Cost Assessment and Program Evaluation, Office of Secretary of Defense and the...for Dams ( CRM -D), developed by the U.S. Army Corps of Engineers (USACE) in collaboration with the Institute for Defense Analyses (IDA) and the U.S...and cyber security risks across a portfolio of dams, and informing decisions on how to mitigate those risks. The CRM -D can effectively quantify the

An Interprofessional Model for Serving Youth at Risk for Substance Abuse: The Team Case Study.

ERIC Educational Resources Information Center

Cobia, Debra C.; And Others

1995-01-01

Three models of interprofessional education appropriate for serving youth at risk for substance abuse are described. The evaluation of the team case study model indicated that the participants were more sensitive to the needs of the youths, experienced increased comfort in consulting other agents, and were more confident in their ability to select…

Assessing evidence for behaviour change affecting the course of HIV epidemics: a new mathematical modelling approach and application to data from Zimbabwe.

PubMed

Hallett, Timothy B; Gregson, Simon; Mugurungi, Owen; Gonese, Elizabeth; Garnett, Geoff P

2009-06-01

Determining whether interventions to reduce HIV transmission have worked is essential, but complicated by the potential for generalised epidemics to evolve over time without individuals changing risk behaviour. We aimed to develop a method to evaluate evidence for changes in risk behaviour altering the course of an HIV epidemic. We developed a mathematical model of HIV transmission, incorporating the potential for natural changes in the epidemic as it matures and the introduction of antiretroviral treatment, and applied a Bayesian Melding framework, in which the model and observed trends in prevalence can be compared. We applied the model to Zimbabwe, using HIV prevalence estimates from antenatal clinic surveillance and house-hold based surveys, and basing model parameters on data from sexual behaviour surveys. There was strong evidence for reductions in risk behaviour stemming HIV transmission. We estimate these changes occurred between 1999 and 2004 and averted 660,000 (95% credible interval: 460,000-860,000) infections by 2008. The model and associated analysis framework provide a robust way to evaluate the evidence for changes in risk behaviour affecting the course of HIV epidemics, avoiding confounding by the natural evolution of HIV epidemics.

Monitoring risk-adjusted outcomes in congenital heart surgery: does the appropriateness of a risk model change with time?

PubMed

Tsang, Victor T; Brown, Katherine L; Synnergren, Mats Johanssen; Kang, Nicholas; de Leval, Marc R; Gallivan, Steve; Utley, Martin

2009-02-01

Risk adjustment of outcomes in pediatric congenital heart surgery is challenging due to the great diversity in diagnoses and procedures. We have previously shown that variable life-adjusted display (VLAD) charts provide an effective graphic display of risk-adjusted outcomes in this specialty. A question arises as to whether the risk model used remains appropriate over time. We used a recently developed graphic technique to evaluate the performance of an existing risk model among those patients at a single center during 2000 to 2003 originally used in model development. We then compared the distribution of predicted risk among these patients with that among patients in 2004 to 2006. Finally, we constructed a VLAD chart of risk-adjusted outcomes for the latter period. Among 1083 patients between April 2000 and March 2003, the risk model performed well at predicted risks above 3%, underestimated mortality at 2% to 3% predicted risk, and overestimated mortality below 2% predicted risk. There was little difference in the distribution of predicted risk among these patients and among 903 patients between June 2004 and October 2006. Outcomes for the more recent period were appreciably better than those expected according to the risk model. This finding cannot be explained by any apparent bias in the risk model combined with changes in case-mix. Risk models can, and hopefully do, become out of date. There is scope for complacency in the risk-adjusted audit if the risk model used is not regularly recalibrated to reflect changing standards and expectations.

Risk factors for invasive fungal disease in critically ill adult patients: a systematic review.

PubMed

Muskett, Hannah; Shahin, Jason; Eyres, Gavin; Harvey, Sheila; Rowan, Kathy; Harrison, David

2011-01-01

Over 5,000 cases of invasive Candida species infections occur in the United Kingdom each year, and around 40% of these cases occur in critical care units. Invasive fungal disease (IFD) in critically ill patients is associated with increased morbidity and mortality at a cost to both the individual and the National Health Service. In this paper, we report the results of a systematic review performed to identify and summarise the important risk factors derived from published multivariable analyses, risk prediction models and clinical decision rules for IFD in critically ill adult patients to inform the primary data collection for the Fungal Infection Risk Evaluation Study. An internet search was performed to identify articles which investigated risk factors, risk prediction models or clinical decisions rules for IFD in critically ill adult patients. Eligible articles were identified in a staged process and were assessed by two investigators independently. The methodological quality of the reporting of the eligible articles was assessed using a set of questions addressing both general and statistical methodologies. Thirteen articles met the inclusion criteria, of which eight articles examined risk factors, four developed a risk prediction model or clinical decision rule and one evaluated a clinical decision rule. Studies varied in terms of objectives, risk factors, definitions and outcomes. The following risk factors were found in multiple studies to be significantly associated with IFD: surgery, total parenteral nutrition, fungal colonisation, renal replacement therapy, infection and/or sepsis, mechanical ventilation, diabetes, and Acute Physiology and Chronic Health Evaluation II (APACHE II) or APACHE III score. Several other risk factors were also found to be statistically significant in single studies only. Risk factor selection process and modelling strategy also varied across studies, and sample sizes were inadequate for obtaining reliable estimates. This review shows a number of risk factors to be significantly associated with the development of IFD in critically ill adults. Methodological limitations were identified in the design and conduct of studies in this area, and caution should be used in their interpretation.

Risk factors for invasive fungal disease in critically ill adult patients: a systematic review

PubMed Central

2011-01-01

Introduction Over 5,000 cases of invasive Candida species infections occur in the United Kingdom each year, and around 40% of these cases occur in critical care units. Invasive fungal disease (IFD) in critically ill patients is associated with increased morbidity and mortality at a cost to both the individual and the National Health Service. In this paper, we report the results of a systematic review performed to identify and summarise the important risk factors derived from published multivariable analyses, risk prediction models and clinical decision rules for IFD in critically ill adult patients to inform the primary data collection for the Fungal Infection Risk Evaluation Study. Methods An internet search was performed to identify articles which investigated risk factors, risk prediction models or clinical decisions rules for IFD in critically ill adult patients. Eligible articles were identified in a staged process and were assessed by two investigators independently. The methodological quality of the reporting of the eligible articles was assessed using a set of questions addressing both general and statistical methodologies. Results Thirteen articles met the inclusion criteria, of which eight articles examined risk factors, four developed a risk prediction model or clinical decision rule and one evaluated a clinical decision rule. Studies varied in terms of objectives, risk factors, definitions and outcomes. The following risk factors were found in multiple studies to be significantly associated with IFD: surgery, total parenteral nutrition, fungal colonisation, renal replacement therapy, infection and/or sepsis, mechanical ventilation, diabetes, and Acute Physiology and Chronic Health Evaluation II (APACHE II) or APACHE III score. Several other risk factors were also found to be statistically significant in single studies only. Risk factor selection process and modelling strategy also varied across studies, and sample sizes were inadequate for obtaining reliable estimates. Conclusions This review shows a number of risk factors to be significantly associated with the development of IFD in critically ill adults. Methodological limitations were identified in the design and conduct of studies in this area, and caution should be used in their interpretation. PMID:22126425

Wild Fire Risk Map in the Eastern Steppe of Mongolia Using Spatial Multi-Criteria Analysis

NASA Astrophysics Data System (ADS)

Nasanbat, Elbegjargal; Lkhamjav, Ochirkhuyag

2016-06-01

Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.

Evaluation of risk from acts of terrorism :the adversary/defender model using belief and fuzzy sets.

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

Darby, John L.

Risk from an act of terrorism is a combination of the likelihood of an attack, the likelihood of success of the attack, and the consequences of the attack. The considerable epistemic uncertainty in each of these three factors can be addressed using the belief/plausibility measure of uncertainty from the Dempster/Shafer theory of evidence. The adversary determines the likelihood of the attack. The success of the attack and the consequences of the attack are determined by the security system and mitigation measures put in place by the defender. This report documents a process for evaluating risk of terrorist acts using anmore » adversary/defender model with belief/plausibility as the measure of uncertainty. Also, the adversary model is a linguistic model that applies belief/plausibility to fuzzy sets used in an approximate reasoning rule base.« less

Risk assessment of turbine rotor failure using probabilistic ultrasonic non-destructive evaluations

NASA Astrophysics Data System (ADS)

Guan, Xuefei; Zhang, Jingdan; Zhou, S. Kevin; Rasselkorde, El Mahjoub; Abbasi, Waheed A.

2014-02-01

The study presents a method and application of risk assessment methodology for turbine rotor fatigue failure using probabilistic ultrasonic nondestructive evaluations. A rigorous probabilistic modeling for ultrasonic flaw sizing is developed by incorporating the model-assisted probability of detection, and the probability density function (PDF) of the actual flaw size is derived. Two general scenarios, namely the ultrasonic inspection with an identified flaw indication and the ultrasonic inspection without flaw indication, are considered in the derivation. To perform estimations for fatigue reliability and remaining useful life, uncertainties from ultrasonic flaw sizing and fatigue model parameters are systematically included and quantified. The model parameter PDF is estimated using Bayesian parameter estimation and actual fatigue testing data. The overall method is demonstrated using a realistic application of steam turbine rotor, and the risk analysis under given safety criteria is provided to support maintenance planning.

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

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.

Discrimination measures for survival outcomes: connection between the AUC and the predictiveness curve.

PubMed

Viallon, Vivian; Latouche, Aurélien

2011-03-01

Finding out biomarkers and building risk scores to predict the occurrence of survival outcomes is a major concern of clinical epidemiology, and so is the evaluation of prognostic models. In this paper, we are concerned with the estimation of the time-dependent AUC--area under the receiver-operating curve--which naturally extends standard AUC to the setting of survival outcomes and enables to evaluate the discriminative power of prognostic models. We establish a simple and useful relation between the predictiveness curve and the time-dependent AUC--AUC(t). This relation confirms that the predictiveness curve is the key concept for evaluating calibration and discrimination of prognostic models. It also highlights that accurate estimates of the conditional absolute risk function should yield accurate estimates for AUC(t). From this observation, we derive several estimators for AUC(t) relying on distinct estimators of the conditional absolute risk function. An empirical study was conducted to compare our estimators with the existing ones and assess the effect of model misspecification--when estimating the conditional absolute risk function--on the AUC(t) estimation. We further illustrate the methodology on the Mayo PBC and the VA lung cancer data sets. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Detection of high GS risk group prostate tumors by diffusion tensor imaging and logistic regression modelling.

PubMed

Ertas, Gokhan

2018-07-01

To assess the value of joint evaluation of diffusion tensor imaging (DTI) measures by using logistic regression modelling to detect high GS risk group prostate tumors. Fifty tumors imaged using DTI on a 3 T MRI device were analyzed. Regions of interests focusing on the center of tumor foci and noncancerous tissue on the maps of mean diffusivity (MD) and fractional anisotropy (FA) were used to extract the minimum, the maximum and the mean measures. Measure ratio was computed by dividing tumor measure by noncancerous tissue measure. Logistic regression models were fitted for all possible pair combinations of the measures using 5-fold cross validation. Systematic differences are present for all MD measures and also for all FA measures in distinguishing the high risk tumors [GS ≥ 7(4 + 3)] from the low risk tumors [GS ≤ 7(3 + 4)] (P < 0.05). Smaller value for MD measures and larger value for FA measures indicate the high risk. The models enrolling the measures achieve good fits and good classification performances (R 2 adj  = 0.55-0.60, AUC = 0.88-0.91), however the models using the measure ratios perform better (R 2 adj  = 0.59-0.75, AUC = 0.88-0.95). The model that employs the ratios of minimum MD and maximum FA accomplishes the highest sensitivity, specificity and accuracy (Se = 77.8%, Sp = 96.9% and Acc = 90.0%). Joint evaluation of MD and FA diffusion tensor imaging measures is valuable to detect high GS risk group peripheral zone prostate tumors. However, use of the ratios of the measures improves the accuracy of the detections substantially. Logistic regression modelling provides a favorable solution for the joint evaluations easily adoptable in clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.

Modeling perceptions of climatic risk in crop production.

PubMed

Reinmuth, Evelyn; Parker, Phillip; Aurbacher, Joachim; Högy, Petra; Dabbert, Stephan

2017-01-01

In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of "still-good yield" (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis.

Modeling perceptions of climatic risk in crop production

PubMed Central

Parker, Phillip; Aurbacher, Joachim; Högy, Petra; Dabbert, Stephan

2017-01-01

In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of “still-good yield” (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis. PMID:28763471

Bayesian algorithm implementation in a real time exposure assessment model on benzene with calculation of associated cancer risks.

PubMed

Sarigiannis, Dimosthenis A; Karakitsios, Spyros P; Gotti, Alberto; Papaloukas, Costas L; Kassomenos, Pavlos A; Pilidis, Georgios A

2009-01-01

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.

Bayesian Algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks

PubMed Central

Sarigiannis, Dimosthenis A.; Karakitsios, Spyros P.; Gotti, Alberto; Papaloukas, Costas L.; Kassomenos, Pavlos A.; Pilidis, Georgios A.

2009-01-01

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations. PMID:22399936

[Chemical risk in farming].

PubMed

Moretto, Angelo

2013-01-01

The most important chemical risks in agriculture are plant protection products. Exposure evaluation in agriculture is not an easy task and cannot be carried out with the tools and methodologies of industrial exposures. However, toxicological studies on plant protection products, that are compulsory, provide a lot of useful information for actual risk assessment. Exposure evaluation can be carried out on the basis of exposure models and on semiquantitative measures based on the observation of the activity as it is carried our by the farmer. It is therefore possible to develop risk profiles that can guide exposure evaluation and health surveillance. Concentrated animal feeding operations are associated with several chemical risks including disinfectants, antibiotics, and gases such as ammonia and hydrogen sulfide, in addition to organic dusts and endotoxins.

Earth Sciences Data and Information System (ESDIS) program planning and evaluation methodology development

NASA Technical Reports Server (NTRS)

Dickinson, William B.

1995-01-01

An Earth Sciences Data and Information System (ESDIS) Project Management Plan (PMP) is prepared. An ESDIS Project Systems Engineering Management Plan (SEMP) consistent with the developed PMP is also prepared. ESDIS and related EOS program requirements developments, management and analysis processes are evaluated. Opportunities to improve the effectiveness of these processes and program/project responsiveness to requirements are identified. Overall ESDIS cost estimation processes are evaluated, and recommendations to improve cost estimating and modeling techniques are developed. ESDIS schedules and scheduling tools are evaluated. Risk assessment, risk mitigation strategies and approaches, and use of risk information in management decision-making are addressed.

Causal modelling applied to the risk assessment of a wastewater discharge.

PubMed

Paul, Warren L; Rokahr, Pat A; Webb, Jeff M; Rees, Gavin N; Clune, Tim S

2016-03-01

Bayesian networks (BNs), or causal Bayesian networks, have become quite popular in ecological risk assessment and natural resource management because of their utility as a communication and decision-support tool. Since their development in the field of artificial intelligence in the 1980s, however, Bayesian networks have evolved and merged with structural equation modelling (SEM). Unlike BNs, which are constrained to encode causal knowledge in conditional probability tables, SEMs encode this knowledge in structural equations, which is thought to be a more natural language for expressing causal information. This merger has clarified the causal content of SEMs and generalised the method such that it can now be performed using standard statistical techniques. As it was with BNs, the utility of this new generation of SEM in ecological risk assessment will need to be demonstrated with examples to foster an understanding and acceptance of the method. Here, we applied SEM to the risk assessment of a wastewater discharge to a stream, with a particular focus on the process of translating a causal diagram (conceptual model) into a statistical model which might then be used in the decision-making and evaluation stages of the risk assessment. The process of building and testing a spatial causal model is demonstrated using data from a spatial sampling design, and the implications of the resulting model are discussed in terms of the risk assessment. It is argued that a spatiotemporal causal model would have greater external validity than the spatial model, enabling broader generalisations to be made regarding the impact of a discharge, and greater value as a tool for evaluating the effects of potential treatment plant upgrades. Suggestions are made on how the causal model could be augmented to include temporal as well as spatial information, including suggestions for appropriate statistical models and analyses.

Effects and Risk Evaluation of Oil Spillage in the Sea Areas of Changxing Island

PubMed Central

Wang, Hanxi; Xu, Jianling; Zhao, Wenkui; Zhang, Jiquan

2014-01-01

This paper evaluated the oil spillage risk in the waters near the island of Changxing in Dalian (China) based on the established risk assessment index. Four wind regimes (windless, northerly wind, westerly wind and southerly wind) were selected as weather conditions for the dynamic prediction of oil drift. If an oil spill occurs near the Koumen (a place near the island of Changxing), the forecast and evaluation are conducted based on a three-dimensional mathematical model of oil spillage, and the results obtained show the scope of the affected area when winds from various directions are applied. The oil spillage would, under various conditions, flow into the northern and western sea area of Changxing Island Bay, namely the Dalian harbor seal National Nature Reserve, and create adverse effects on the marine ecological environment. The rationality of combining the established oil spillage risk comprehensive index system with model prediction is further confirmed. Finally, preventive measures and quick fixes are presented in the case of accidental oil spillages. The most effective method to reduce environment risk is to adopt reasonable preventive measures and quick fixes. PMID:25153473

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.

Common carotid artery intima-media thickness is as good as carotid intima-media thickness of all carotid artery segments in improving prediction of coronary heart disease risk in the Atherosclerosis Risk in Communities (ARIC) study.

PubMed

Nambi, Vijay; Chambless, Lloyd; He, Max; Folsom, Aaron R; Mosley, Tom; Boerwinkle, Eric; Ballantyne, Christie M

2012-01-01

Carotid intima-media thickness (CIMT) and plaque information can improve coronary heart disease (CHD) risk prediction when added to traditional risk factors (TRF). However, obtaining adequate images of all carotid artery segments (A-CIMT) may be difficult. Of A-CIMT, the common carotid artery intima-media thickness (CCA-IMT) is relatively more reliable and easier to measure. We evaluated whether CCA-IMT is comparable to A-CIMT when added to TRF and plaque information in improving CHD risk prediction in the Atherosclerosis Risk in Communities (ARIC) study. Ten-year CHD risk prediction models using TRF alone, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque were developed for the overall cohort, men, and women. The area under the receiver operator characteristic curve (AUC), per cent individuals reclassified, net reclassification index (NRI), and model calibration by the Grønnesby-Borgan test were estimated. There were 1722 incident CHD events in 12 576 individuals over a mean follow-up of 15.2 years. The AUC for TRF only, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque models were 0.741, 0.754, and 0.753, respectively. Although there was some discordance when the CCA-IMT + plaque- and A-CIMT + plaque-based risk estimation was compared, the NRI and clinical NRI (NRI in the intermediate-risk group) when comparing the CIMT models with TRF-only model, per cent reclassified, and test for model calibration were not significantly different. Coronary heart disease risk prediction can be improved by adding A-CIMT + plaque or CCA-IMT + plaque information to TRF. Therefore, evaluating the carotid artery for plaque presence and measuring CCA-IMT, which is easier and more reliable than measuring A-CIMT, provide a good alternative to measuring A-CIMT for CHD risk prediction.

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.

[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.

Long-Term Post-CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions.

PubMed

Carr, Brendan M; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C; Zhu, Wei; Shroyer, A Laurie

2016-01-01

Clinical risk models are commonly used to predict short-term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long-term mortality. The added value of long-term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long-term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Long-term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c-index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Mortality rates were 3%, 9%, and 17% at one-, three-, and five years, respectively (median follow-up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long-term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Long-term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long-term mortality risk can be accurately assessed and subgroups of higher-risk patients can be identified for enhanced follow-up care. More research appears warranted to refine long-term CABG clinical risk models. © 2015 The Authors. Journal of Cardiac Surgery Published by Wiley Periodicals, Inc.

Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

PubMed Central

Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

2015-01-01

Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.

PubMed

Koivu, Aki; Korpimäki, Teemu; Kivelä, Petri; Pahikkala, Tapio; Sairanen, Mikko

2018-05-04

Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population. Copyright © 2018 Elsevier Ltd. All rights reserved.

Occupational health and safety: Designing and building with MACBETH a value risk-matrix for evaluating health and safety risks

NASA Astrophysics Data System (ADS)

Lopes, D. F.; Oliveira, M. D.; Costa, C. A. Bana e.

2015-05-01

Risk matrices (RMs) are commonly used to evaluate health and safety risks. Nonetheless, they violate some theoretical principles that compromise their feasibility and use. This study describes how multiple criteria decision analysis methods have been used to improve the design and the deployment of RMs to evaluate health and safety risks at the Occupational Health and Safety Unit (OHSU) of the Regional Health Administration of Lisbon and Tagus Valley. ‘Value risk-matrices’ (VRMs) are built with the MACBETH approach in four modelling steps: a) structuring risk impacts, involving the construction of descriptors of impact that link risk events with health impacts and are informed by scientific evidence; b) generating a value measurement scale of risk impacts, by applying the MACBETH-Choquet procedure; c) building a system for eliciting subjective probabilities that makes use of a numerical probability scale that was constructed with MACBETH qualitative judgments on likelihood; d) and defining a classification colouring scheme for the VRM. A VRM built with OHSU members was implemented in a decision support system which will be used by OHSU members to evaluate health and safety risks and to identify risk mitigation actions.

Risk factors for pressure ulcer development in critically Ill patients: a conceptual model to guide research.

PubMed

Benoit, Richard; Mion, Lorraine

2012-08-01

This paper presents a proposed conceptual model to guide research on pressure ulcer risk in critically ill patients, who are at high risk for pressure ulcer development. However, no conceptual model exists that guides risk assessment in this population. Results from a review of prospective studies were evaluated for design quality and level of statistical reporting. Multivariate findings from studies having high or medium design quality by the National Institute of Health and Clinical Excellence standards were conceptually grouped. The conceptual groupings were integrated into Braden and Bergstrom's (Braden and Bergstrom [1987] Rehabilitation Nursing, 12, 8-12, 16) conceptual model, retaining their original constructs and augmenting their concept of intrinsic factors for tissue tolerance. The model could enhance consistency in research on pressure ulcer risk factors. Copyright © 2012 Wiley Periodicals, Inc.

Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus.

PubMed

Cohen, Mark E; Ko, Clifford Y; Bilimoria, Karl Y; Zhou, Lynn; Huffman, Kristopher; Wang, Xue; Liu, Yaoming; Kraemer, Kari; Meng, Xiangju; Merkow, Ryan; Chow, Warren; Matel, Brian; Richards, Karen; Hart, Amy J; Dimick, Justin B; Hall, Bruce L

2013-08-01

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) collects detailed clinical data from participating hospitals using standardized data definitions, analyzes these data, and provides participating hospitals with reports that permit risk-adjusted comparisons with a surgical quality standard. Since its inception, the ACS NSQIP has worked to refine surgical outcomes measurements and enhance statistical methods to improve the reliability and validity of this hospital profiling. From an original focus on controlling for between-hospital differences in patient risk factors with logistic regression, ACS NSQIP has added a variable to better adjust for the complexity and risk profile of surgical procedures (procedure mix adjustment) and stabilized estimates derived from small samples by using a hierarchical model with shrinkage adjustment. New models have been developed focusing on specific surgical procedures (eg, "Procedure Targeted" models), which provide opportunities to incorporate indication and other procedure-specific variables and outcomes to improve risk adjustment. In addition, comparative benchmark reports given to participating hospitals have been expanded considerably to allow more detailed evaluations of performance. Finally, procedures have been developed to estimate surgical risk for individual patients. This article describes the development of, and justification for, these new statistical methods and reporting strategies in ACS NSQIP. Copyright © 2013 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

PubMed Central

2012-01-01

Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting. PMID:22417403

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

PubMed

Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

2012-03-14

Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.

A spatial assessment framework for evaluating flood risk under extreme climates.

PubMed

Chen, Yun; Liu, Rui; Barrett, Damian; Gao, Lei; Zhou, Mingwei; Renzullo, Luigi; Emelyanova, Irina

2015-12-15

Australian coal mines have been facing a major challenge of increasing risk of flooding caused by intensive rainfall events in recent years. In light of growing climate change concerns and the predicted escalation of flooding, estimating flood inundation risk becomes essential for understanding sustainable mine water management in the Australian mining sector. This research develops a spatial multi-criteria decision making prototype for the evaluation of flooding risk at a regional scale using the Bowen Basin and its surroundings in Queensland as a case study. Spatial gridded data, including climate, hydrology, topography, vegetation and soils, were collected and processed in ArcGIS. Several indices were derived based on time series of observations and spatial modeling taking account of extreme rainfall, evapotranspiration, stream flow, potential soil water retention, elevation and slope generated from a digital elevation model (DEM), as well as drainage density and proximity extracted from a river network. These spatial indices were weighted using the analytical hierarchy process (AHP) and integrated in an AHP-based suitability assessment (AHP-SA) model under the spatial risk evaluation framework. A regional flooding risk map was delineated to represent likely impacts of criterion indices at different risk levels, which was verified using the maximum inundation extent detectable by a time series of remote sensing imagery. The result provides baseline information to help Bowen Basin coal mines identify and assess flooding risk when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in this research offers the Australian mining industry, and social and environmental studies around the world, an effective way to produce reliable assessment on flood risk for managing uncertainty in water availability under climate change. Copyright © 2015. Published by Elsevier B.V.

Integrating human health and ecological data into cumulative risk assessment through the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks

EPA Science Inventory

Cumulative risk assessment (CRA) methods promote the use of a conceptual site model (CSM) to apportion exposures and integrate risk from relevant stressors across different species. Integration is important to provide a more complete assessment of risk, but evaluating endpoints a...

Consumption Risk and the Cross Section of Expected Returns.

ERIC Educational Resources Information Center

Parker, Jonathan A.; Julliard, Christian

2005-01-01

This paper evaluates the central insight of the consumption capital asset pricing model that an asset's expected return is determined by its equilibrium risk to consumption. Rather than measure risk by the contemporaneous covariance of an asset's return and consumption growth, we measure risk by the covariance of an asset's return and consumption…

Risk Reduction and Resource Pooling on a Cooperation Task

ERIC Educational Resources Information Center

Pietras, Cynthia J.; Cherek, Don R.; Lane, Scott D.; Tcheremissine, Oleg

2006-01-01

Two experiments investigated choice in adult humans on a simulated cooperation task to evaluate a risk-reduction account of sharing based on the energy-budget rule. The energy-budget rule is an optimal foraging model that predicts risk-averse choices when net energy gains exceed energy requirements (positive energy budget) and risk-prone choices…

Cost-benefit analysis of biopsy methods for suspicious mammographic lesions; discussion 994-5.

PubMed

Fahy, B N; Bold, R J; Schneider, P D; Khatri, V; Goodnight, J E

2001-09-01

Stereotactic core biopsy (SCB) is more cost-effective than needle-localized biopsy (NLB) for evaluation and treatment of mammographic lesions. A computer-generated mathematical model was developed based on clinical outcome modeling to estimate costs accrued during evaluation and treatment of suspicious mammographic lesions. Total costs were determined for evaluation and subsequent treatment of cancer when either SCB or NLB was used as the initial biopsy method. Cost was estimated by the cumulative work relative value units accrued. The risk of malignancy based on the Breast Imaging Reporting Data System (BIRADS) score and mammographic suspicion of ductal carcinoma in situ were varied to simulate common clinical scenarios. Total cost accumulated during evaluation and subsequent surgical therapy (if required). Evaluation of BIRADS 5 lesions (highly suggestive, risk of malignancy = 90%) resulted in equivalent relative value units for both techniques (SCB, 15.54; NLB, 15.47). Evaluation of lesions highly suspicious for ductal carcinoma in situ yielded similar total treatment relative value units (SCB, 11.49; NLB, 10.17). Only for evaluation of BIRADS 4 lesions (suspicious abnormality, risk of malignancy = 34%) was SCB more cost-effective than NLB (SCB, 7.65 vs. NLB, 15.66). No difference in cost-benefit was found when lesions highly suggestive of malignancy (BIRADS 5) or those suspicious for ductal carcinoma in situ were evaluated initially with SCB vs. NLB, thereby disproving the hypothesis. Only for intermediate-risk lesions (BIRADS 4) did initial evaluation with SCB yield a greater cost savings than with NLB.

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

PubMed

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

2010-01-01

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

Persistent hemifacial spasm after microvascular decompression: a risk assessment model.

PubMed

Shah, Aalap; Horowitz, Michael

2017-06-01

Microvascular decompression (MVD) for hemifacial spasm (HFS) provides resolution of disabling symptoms such as eyelid twitching and muscle contractions of the entire hemiface. The primary aim of this study was to evaluate the predictive value of patient demographics and spasm characteristics on long-term outcomes, with or without intraoperative lateral spread response (LSR) as an additional variable in a risk assessment model. A retrospective study was undertaken to evaluate the associations of pre-operative patient characteristics, as well as intraoperative LSR and need for a staged procedure on the presence of persistent or recurrent HFS at the time of hospital discharge and at follow-up. A risk assessment model was constructed with the inclusion of six clinically or statistically significant variables from the univariate analyses. A receiving operator characteristic curve was generated, and area under the curve was calculated to determine the strength of the predictive model. A risk assessment model was first created consisting of significant pre-operative variables (Model 1) (age >50, female gender, history of botulinum toxin use, platysma muscle involvement). This model demonstrated borderline predictive value for persistent spasm at discharge (AUC .60; p=.045) and fair predictive value at follow-up (AUC .75; p=.001). Intraoperative variables (e.g. LSR persistence) demonstrated little additive value (Model 2) (AUC .67). Patients with a higher risk score (three or greater) demonstrated greater odds of persistent HFS at the time of discharge (OR 1.5 [95%CI 1.16-1.97]; p=.035), as well as greater odds of persistent or recurrent spasm at the time of follow-up (OR 3.0 [95%CI 1.52-5.95]; p=.002) Conclusions: A risk assessment model consisting of pre-operative clinical characteristics is useful in prognosticating HFS persistence at follow-up.

Bioconcentration of gaseous organic chemicals in plant leaves: Comparison of experimental data with model predictions

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

Polder, M.D.; Hulzebos, E.M.; Jager, D.T.

1998-01-01

This literature study is performed to support the implementation of two models in a risk assessment system for the evaluation of chemicals and their risk for human health and the environment. One of the exposure pathways for humans and cattle is the uptake of chemicals by plants. In this risk assessment system the transfer of gaseous organic substances from air to plants modeled by Riederer is included. A similar model with a more refined approach, including dilution by growth, is proposed by Trapp and Matthies, which was implemented in the European version of this risk assessment system (EUSES). In thismore » study both models are evaluated by comparison with experimental data on leaf/air partition coefficients found in the literature. For herbaceous plants both models give good estimations for the leaf/air partition coefficient up to 10{sup 7}, with deviations for most substances within a factor of five. For the azalea and spruce group the fit between experimental BCF values and the calculated model values is less adequate. For substances for which Riederer estimates a leaf/air partition coefficient above 10{sup 7}, the approach of Trapp and Matthies seems more adequate; however, few data were available.« less

An Overview of Quantitative Risk Assessment of Space Shuttle Propulsion Elements

NASA Technical Reports Server (NTRS)

Safie, Fayssal M.

1998-01-01

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

Evaluation of health risks for contaminated aquifers.

PubMed Central

Piver, W T; Jacobs, T L; Medina, M A

1997-01-01

This review focuses on progress in the development of transport models for heterogeneous contaminated aquifers, the use of predicted contaminant concentrations in groundwater for risk assessment for heterogeneous human populations, and the evaluation of aquifer remediation technologies. Major limitations and areas for continuing research for all methods presented in this review are identified. Images Figure 2. PMID:9114282

Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

PubMed

Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

2017-03-15

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.

European approaches to work-related stress: a critical review on risk evaluation.

PubMed

Zoni, Silvia; Lucchini, Roberto G

2012-03-01

In recent years, various international organizations have raised awareness regarding psychosocial risks and work-related stress. European stakeholders have also taken action on these issues by producing important documents, such as position papers and government regulations, which are reviewed in this article. In particular, 4 European models that have been developed for the assessment and management of work-related stress are considered here. Although important advances have been made in the understanding of work-related stress, there are still gaps in the translation of this knowledge into effective practice at the enterprise level. There are additional problems regarding the methodology in the evaluation of work-related stress. The European models described in this article are based on holistic, global and participatory approaches, where the active role of and involvement of workers are always emphasized. The limitations of these models are in the lack of clarity on preventive intervention and, for two of them, the lack of instrument standardization for risk evaluation. The comparison among the European models to approach work-related stress, although with limitations and socio-cultural differences, offers the possibility for the development of a social dialogue that is important in defining the correct and practical methodology for work stress evaluation and prevention.

Design of psychosocial factors questionnaires: a systematic measurement approach

PubMed Central

Vargas, Angélica; Felknor, Sarah A

2012-01-01

Background Evaluation of psychosocial factors requires instruments that measure dynamic complexities. This study explains the design of a set of questionnaires to evaluate work and non-work psychosocial risk factors for stress-related illnesses. Methods The measurement model was based on a review of literature. Content validity was performed by experts and cognitive interviews. Pilot testing was carried out with a convenience sample of 132 workers. Cronbach’s alpha evaluated internal consistency and concurrent validity was estimated by Spearman correlation coefficients. Results Three questionnaires were constructed to evaluate exposure to work and non-work risk factors. Content validity improved the questionnaires coherence with the measurement model. Internal consistency was adequate (α=0.85–0.95). Concurrent validity resulted in moderate correlations of psychosocial factors with stress symptoms. Conclusions Questionnaires´ content reflected a wide spectrum of psychosocial factors sources. Cognitive interviews improved understanding of questions and dimensions. The structure of the measurement model was confirmed. PMID:22628068

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history

PubMed Central

Sparks, Jeffrey A.; Chen, Chia-Yen; Jiang, Xia; Askling, Johan; Hiraki, Linda T.; Malspeis, Susan; Klareskog, Lars; Alfredsson, Lars; Costenbader, Karen H.; Karlson, Elizabeth W.

2014-01-01

Objective To develop and validate rheumatoid arthritis (RA) risk models based on family history, epidemiologic factors, and known genetic risk factors. Methods We developed and validated models for RA based on known RA risk factors, among women in two cohorts: the Nurses’ Health Study (NHS, 381 RA cases and 410 controls) and the Epidemiological Investigation of RA (EIRA, 1244 RA cases and 971 controls). Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC) in logistic regression models for the study population and for those with positive family history. The joint effect of family history with genetics, smoking, and body mass index (BMI) was evaluated using logistic regression models to estimate odds ratios (OR) for RA. Results The complete model including family history, epidemiologic risk factors, and genetics demonstrated AUCs of 0.74 for seropositive RA in NHS and 0.77 for anti-citrullinated protein antibody (ACPA)-positive RA in EIRA. Among women with positive family history, discrimination was excellent for complete models for seropositive RA in NHS (AUC 0.82) and ACPA-positive RA in EIRA (AUC 0.83). Positive family history, high genetic susceptibility, smoking, and increased BMI had an OR of 21.73 for ACPA-positive RA. Conclusions We developed models for seropositive and seronegative RA phenotypes based on family history, epidemiologic and genetic factors. Among those with positive family history, models utilizing epidemiologic and genetic factors were highly discriminatory for seropositive and seronegative RA. Assessing epidemiological and genetic factors among those with positive family history may identify individuals suitable for RA prevention strategies. PMID:24685909

Models of evaluating efficiency and risks on integration of cloud-base IT-services of the machine-building enterprise: a system approach

NASA Astrophysics Data System (ADS)

Razumnikov, S.; Kurmanbay, A.

2016-04-01

The present paper suggests a system approach to evaluation of the effectiveness and risks resulted from the integration of cloud-based services in a machine-building enterprise. This approach makes it possible to estimate a set of enterprise IT applications and choose the applications to be migrated to the cloud with regard to specific business requirements, a technological strategy and willingness to risk.

Climate Influence on Emerging Risk Areas for Rift Valley Fever Epidemics in Tanzania.

PubMed

Mweya, Clement N; Mboera, Leonard E G; Kimera, Sharadhuli I

2017-07-01

Rift Valley Fever (RVF) is a climate-related arboviral infection of animals and humans. Climate is thought to represent a threat toward emerging risk areas for RVF epidemics globally. The objective of this study was to evaluate influence of climate on distribution of suitable breeding habitats for Culex pipiens complex, potential mosquito vector responsible for transmission and distribution of disease epidemics risk areas in Tanzania. We used ecological niche models to estimate potential distribution of disease risk areas based on vectors and disease co-occurrence data approach. Climatic variables for the current and future scenarios were used as model inputs. Changes in mosquito vectors' habitat suitability in relation to disease risk areas were estimated. We used partial receiver operating characteristic and the area under the curves approach to evaluate model predictive performance and significance. Habitat suitability for Cx. pipiens complex indicated broad-scale potential for change and shift in the distribution of the vectors and disease for both 2020 and 2050 climatic scenarios. Risk areas indicated more intensification in the areas surrounding Lake Victoria and northeastern part of the country through 2050 climate scenario. Models show higher probability of emerging risk areas spreading toward the western parts of Tanzania from northeastern areas and decrease in the southern part of the country. Results presented here identified sites for consideration to guide surveillance and control interventions to reduce risk of RVF disease epidemics in Tanzania. A collaborative approach is recommended to develop and adapt climate-related disease control and prevention strategies.

Development of a Frost Risk Assessment Tool in Agriculture for a Mediterranean ecosystem Utilizing MODIS satellite observations Geomatics and Surface Data

NASA Astrophysics Data System (ADS)

Louka, Panagiota; Papanikolaou, Ioannis; Petropoulos, George; Migiros, George; Tsiros, Ioannis

2014-05-01

Frost risk in Mediterranean countries is a critical factor in agricultural planning and management. Nowadays, the rapid technological developments in Earth Observation (EO) technology have improved dramatically our ability to map the spatiotemporal distribution of frost conditions over a given area and evaluate its impacts on the environment and society. In this study, a frost risk model for agricultural crops cultivated in a Mediterranean environment has been developed, based primarily on Earth Observation (EO) data from MODIS sensor and ancillary spatial and point data. The ability of the model to predict frost conditions has been validated for selected days on which frost conditions had been observed for a region in Northwestern Greece according to ground observations obtained by the Agricultural Insurance Organization (ELGA). An extensive evaluation of the frost risk model predictions has been performed herein to evaluate objectively its ability to predict the spatio-temporal distribution of frost risk in the studied region, including comparisons against physiographical factors of the study area. The topographical characteristics that were taken under consideration were latitude, altitude, slope steepness, topographic convergence and the extend of the areas influenced by water bodies (such as lake and sea) existing in the study area. Additional data were also used concerning land use data and vegetation classification (type and density). Our results showed that the model was able to produce reasonably the spatio-temporal distribution of the frost conditions in our study area, following largely explainable patterns in respect to the study site and local weather conditions characteristics. All in all, the methodology implemented herein proved capable in obtaining rapidly and cost-effectively cartography of the frost risk in a Mediterranean environment, making it potentially a very useful tool for agricultural management and planning. The model presented here has also a potential to enhance conventional field-based surveying for monitoring frost changes over long timescales. KEYWORDS: Earth Observation, MODIS, frost, risk assessment, Greece

Our trial to develop a risk assessment tool for CO2 geological storage (GERAS-CO2GS)

NASA Astrophysics Data System (ADS)

Tanaka, A.; Sakamoto, Y.; Komai, T.

2012-12-01

We will introduce our researches about to develop a risk assessment tool named 'GERAS-CO2GS' (Geo-environmental Risk Assessment System, CO2 Geological Storage Risk Assessment System) for 'Carbon Dioxide Geological Storage (Geological CCS)'. It aims to facilitate understanding of size of impact of risks related with upper migration of injected CO2. For gaining public recognition about feasibility of Geological CCS, quantitative estimation of risks is essential, to let public knows the level of the risk: whether it is negligible or not. Generally, in preliminary hazard analysis procedure, potential hazards could be identified within Geological CCS's various facilities such as: reservoir, cap rock, upper layers, CO2 injection well, CO2 injection plant and CO2 transport facilities. Among them, hazard of leakage of injected C02 is crucial, because it is the clue to estimate risks around a specific injection plan in terms of safety, environmental protection effect and economy. Our risk assessment tool named GERAS-CO2GS evaluates volume and rate of retention and leakage of injected CO2 in relation with fractures and/or faults, and then it estimates impact of seepages on the surface of the earth. GERAS-CO2GS has four major processing segments: (a) calculation of CO2 retention and leakage volume and rate, (b) data processing of CO2 dispersion on the surface and ambient air, (c) risk data definition and (d) evaluation of risk. Concerning to the injection site, we defined a model, which is consisted from an injection well and a geological strata model: which involves a reservoir, a cap rock, an upper layer, faults, seabed, sea, the surface of the earth and the surface of the sea. For retention rate of each element of CO2 injection site model, we use results of our experimental and numerical studies on CO2 migration within reservoirs and faults with specific lithological conditions. For given CO2 injection rate, GERAS-CO2GS calculates CO2 retention and leakage of each segment of injection site model. It also evaluates dispersion of CO2 on the surface of the earth and ambient air, and displays evaluated risk level on Goole earth contour of risk levels with color classification. As regard with numerical estimation of CO2's surface dispersion, we use ADMER 2.5 (Atmospheric Dispersion Model for Exposure and Risk Assessment, AIST), which assesses ambient dispersion of materials using real observed atmospheric data such as wind direction and temperatures by meteorological observatory. As far as our simulations, it is obvious that cause of Lake Nyos type accident is owes its maar topography of the lake and the volume and duration of the CO2 outburst (about 1 km3). It's unlikely to cause similar happenings in geological CCS site, because there are significant difference amount of CO2 and topography. At this moment, GERAS-CO2GS is prototype system. We are going to extend GERAS-CO2GS functions and evaluate risks of further risk scenarios. Concerning to the route of seabed to sea and the surface of the sea, we hope to implement outer research findings into our logics. In the course of further research, we are going to develop GERAS-CO2GS will be able to estimate broader risks, and to contribute to the efforts for legislations and standards of CO2 Geological storage.

Field evaluation of an avian risk assessment model

USGS Publications Warehouse

Vyas, N.B.; Spann, J.W.; Hulse, C.S.; Borges, S.L.; Bennett, R.S.; Torrez, M.; Williams, B.I.; Leffel, R.

2006-01-01

We conducted two laboratory subacute dietary toxicity tests and one outdoor subacute dietary toxicity test to determine the effectiveness of the U.S. Environmental Protection Agency's deterministic risk assessment model for evaluating the potential of adverse effects to birds in the field. We tested technical-grade diazinon and its D Z N- 50W (50% diazinon active ingredient wettable powder) formulation on Canada goose (Branta canadensis) goslings. Brain acetylcholinesterase activity was measured, and the feathers and skin, feet. and gastrointestinal contents were analyzed for diazinon residues. The dose-response curves showed that diazinon was significantly more toxic to goslings in the outdoor test than in the laboratory tests. The deterministic risk assessment method identified the potential for risk to birds in general, but the factors associated with extrapolating from the laboratory to the field, and from the laboratory test species to other species, resulted in the underestimation of risk to the goslings. The present study indicates that laboratory-based risk quotients should be interpreted with caution.

Pre- and posttest evaluation of a breast cancer risk assessment program for nurse practitioners.

PubMed

Edwards, Quannetta T; Seibert, Diane

2010-07-01

Numerous studies have shown that healthcare providers, including nurse practitioners (NPs) fail to provide breast cancer risk assessment (BrCRA) in primary care settings. A potential barrier to the use of BrCRA is insufficient knowledge or training of risk assessment. The purpose of this study was to analyze the outcome of a BrCRA program developed to enhance NPs' knowledge of risk assessment and use of empiric risk assessment models. Thirty-five NPs participated in a before-after (pretest-posttest design) study evaluating the effectiveness of a BrCRA education program conducted at a national NP conference. Demographics, pre/post knowledge, and course satisfaction measures were all examined as a part of this pilot study. Continuing education through the implementation of a BrCRA program significantly increased NPs knowledge in assessing breast cancer risk and the use of empiric risk assessment models. Many healthcare providers, including NPs, are inadequately prepared to assess a woman's risk for breast cancer. Understanding breast cancer risk assessment is essential if NPs are to provide appropriate counseling, management, and referral strategies needed to reduce a woman's risk for developing the disease. Continuing education provides one means to enhance NP's knowledge of BrCRA.

Contribution of European research to risk analysis.

PubMed

Boenke, A

2001-12-01

The European Commission's, Quality of Life Research Programme, Key Action 1-Health, Food & Nutrition is mission-oriented and aims, amongst other things, at providing a healthy, safe and high-quality food supply leading to reinforced consumer confidence in the safety, of European food. Its objectives also include the enhancing of the competitiveness of the European food supply. Key Action 1 is currently supporting a number of different types of European collaborative projects in the area of risk analysis. The objectives of these projects range from the development and validation of prevention strategies including the reduction of consumers risks; development and validation of new modelling approaches, harmonization of risk assessment principles methodologies and terminology; standardization of methods and systems used for the safety evaluation of transgenic food; providing of tools for the evaluation of human viral contamination of shellfish and quality control; new methodologies for assessing the potential of unintended effects of genetically modified (genetically modified) foods; development of a risk assessment model for Cryptosporidium parvum related to the food and water industries, to the development of a communication platform for genetically modified organism, producers, retailers, regulatory authorities and consumer groups to improve safety assessment procedures, risk management strategies and risk communication; development and validation of new methods for safety testing of transgenic food; evaluation of the safety and efficacy of iron supplementation in pregnant women, evaluation of the potential cancer-preventing activity of pro- and pre-biotic ('synbiotic') combinations in human volunteers. An overview of these projects is presented here.

Geothermal probabilistic cost study

NASA Technical Reports Server (NTRS)

Orren, L. H.; Ziman, G. M.; Jones, S. C.; Lee, T. K.; Noll, R.; Wilde, L.; Sadanand, V.

1981-01-01

A tool is presented to quantify the risks of geothermal projects, the Geothermal Probabilistic Cost Model (GPCM). The GPCM model was used to evaluate a geothermal reservoir for a binary-cycle electric plant at Heber, California. Three institutional aspects of the geothermal risk which can shift the risk among different agents was analyzed. The leasing of geothermal land, contracting between the producer and the user of the geothermal heat, and insurance against faulty performance were examined.

Listeria monocytogenes in Retail Delicatessens: an Interagency Risk Assessment-model and baseline results.

PubMed

Pouillot, Régis; Gallagher, Daniel; Tang, Jia; Hoelzer, Karin; Kause, Janell; Dennis, Sherri B

2015-01-01

The Interagency Risk Assessment-Listeria monocytogenes (Lm) in Retail Delicatessens provides a scientific assessment of the risk of listeriosis associated with the consumption of ready-to-eat (RTE) foods commonly prepared and sold in the delicatessen (deli) of a retail food store. The quantitative risk assessment (QRA) model simulates the behavior of retail employees in a deli department and tracks the Lm potentially present in this environment and in the food. Bacterial growth, bacterial inactivation (following washing and sanitizing actions), and cross-contamination (from object to object, from food to object, or from object to food) are evaluated through a discrete event modeling approach. The QRA evaluates the risk per serving of deli-prepared RTE food for the susceptible and general population, using a dose-response model from the literature. This QRA considers six separate retail baseline conditions and provides information on the predicted risk of listeriosis for each. Among the baseline conditions considered, the model predicts that (i) retail delis without an environmental source of Lm (such as niches), retail delis without niches that do apply temperature control, and retail delis with niches that do apply temperature control lead to lower predicted risk of listeriosis relative to retail delis with niches and (ii) retail delis with incoming RTE foods that are contaminated with Lm lead to higher predicted risk of listeriosis, directly or through cross-contamination, whether the contaminated incoming product supports growth or not. The risk assessment predicts that listeriosis cases associated with retail delicatessens result from a sequence of key events: (i) the contaminated RTE food supports Lm growth; (ii) improper retail and/or consumer storage temperature or handling results in the growth of Lm on the RTE food; and (iii) the consumer of this RTE food is susceptible to listeriosis. The risk assessment model, therefore, predicts that cross-contamination with Lm at retail predominantly results in sporadic cases.

Catastrophe risk data scoping for disaster risk finance in Asia

NASA Astrophysics Data System (ADS)

Millinship, Ian; Revilla-Romero, Beatriz

2017-04-01

Developing countries across Latin America, Africa, and Asia are some of the most exposed to natural catastrophes in the world. Over the last 20 years, Asia has borne almost half the estimated global economic cost of natural disasters - around 53billion annually. Losses from natural disasters can damage growth and hamper economic development and unlike in developed countries where risk is reallocated through re/insurance, typically these countries rely on budget reallocations and donor assistance in order to attempt to meet financing needs. There is currently an active international dialogue on the need to increase access to disaster risk financing solutions in Asia. The World Bank-GFDRR Disaster Risk Financing and Insurance Program with financial support from the Rockefeller Foundation, is currently working to develop regional options for disaster risk financing for developing countries in Asia. The first stage of this process has been to evaluate available catastrophe data suitable to support the design and implementation of disaster risk financing mechanisms in selected Asian countries. This project was carried out by a consortium of JBA Risk Management, JBA Consulting, ImageCat and Cat Risk Intelligence. The project focuses on investigating potential data sources for fourteen selected countries in Asia, for flood, tropical cyclone, earthquake and drought perils. The project was carried out under four stages. The first phase focused to identify and catalogue live/dynamic hazard data sources such as hazard gauging networks, or earth observations datasets which could be used to inform a parametric trigger. Live data sources were identified that provide credibility, transparency, independence, frequent reporting, consistency and stability. Data were catalogued at regional level, and prioritised at local level for five countries: Bangladesh, Indonesia, Pakistan, Sri Lanka and Viet Nam. The second phase was to identify, catalogue and evaluate catastrophe risk models that could quantify risk and provide a view of risk to support design and pricing of parametric disaster risk financing mechanisms. The third stage was to evaluate the usability of data sources and catastrophe models, and to develop index prototypes to outline how data and catastrophe models could be combined using local, regional and global data sources. Finally, the project identified priorities for investment to support the collection, analysis and evaluation of natural catastrophes in order to support disaster risk financing.

Evaluating Computer-Based Assessment in a Risk-Based Model

ERIC Educational Resources Information Center

Zakrzewski, Stan; Steven, Christine; Ricketts, Chris

2009-01-01

There are three purposes for evaluation: evaluation for action to aid the decision making process, evaluation for understanding to further enhance enlightenment and evaluation for control to ensure compliance to standards. This article argues that the primary function of evaluation in the "Catherine Wheel" computer-based assessment (CBA)…

Developing Risk Prediction Models for Kidney Injury and Assessing Incremental Value for Novel Biomarkers

PubMed Central

Kerr, Kathleen F.; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G.

2014-01-01

The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients’ risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. PMID:24855282

Risk factors for the treatment outcome of retreated pulmonary tuberculosis patients in China: an optimized prediction model.

PubMed

Wang, X-M; Yin, S-H; Du, J; Du, M-L; Wang, P-Y; Wu, J; Horbinski, C M; Wu, M-J; Zheng, H-Q; Xu, X-Q; Shu, W; Zhang, Y-J

2017-07-01

Retreatment of tuberculosis (TB) often fails in China, yet the risk factors associated with the failure remain unclear. To identify risk factors for the treatment failure of retreated pulmonary tuberculosis (PTB) patients, we analyzed the data of 395 retreated PTB patients who received retreatment between July 2009 and July 2011 in China. PTB patients were categorized into 'success' and 'failure' groups by their treatment outcome. Univariable and multivariable logistic regression were used to evaluate the association between treatment outcome and socio-demographic as well as clinical factors. We also created an optimized risk score model to evaluate the predictive values of these risk factors on treatment failure. Of 395 patients, 99 (25·1%) were diagnosed as retreatment failure. Our results showed that risk factors associated with treatment failure included drug resistance, low education level, low body mass index (6 months), standard treatment regimen, retreatment type, positive culture result after 2 months of treatment, and the place where the first medicine was taken. An Optimized Framingham risk model was then used to calculate the risk scores of these factors. Place where first medicine was taken (temporary living places) received a score of 6, which was highest among all the factors. The predicted probability of treatment failure increases as risk score increases. Ten out of 359 patients had a risk score >9, which corresponded to an estimated probability of treatment failure >70%. In conclusion, we have identified multiple clinical and socio-demographic factors that are associated with treatment failure of retreated PTB patients. We also created an optimized risk score model that was effective in predicting the retreatment failure. These results provide novel insights for the prognosis and improvement of treatment for retreated PTB patients.

Methodology for back-contamination risk assessment for a Mars sample return mission

NASA Technical Reports Server (NTRS)

Merkhofer, M. W.; Quinn, D. J.

1977-01-01

The risk of back-contamination from Mars Surface Sample Return (MSSR) missions is assessed. The methodology is designed to provide an assessment of the probability that a given mission design and strategy will result in accidental release of Martian organisms acquired as a result of MSSR. This is accomplished through the construction of risk models describing the mission risk elements and their impact on back-contamination probability. A conceptual framework is presented for using the risk model to evaluate mission design decisions that require a trade-off between science and planetary protection considerations.

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.

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.

Regional Risk Evaluation of Flood Disasters for the Trunk-Highway in Shaanxi, China

PubMed Central

Qi, Hong-Liang; Tian, Wei-Ping; Li, Jia-Chun

2015-01-01

Due to the complicated environment there are various types of highway disasters in Shaanxi Province (China). The damages caused are severe, losses are heavy, and have rapidly increased over the years, especially those caused by flood disasters along the rivers in mountainous areas. Therefore, research on risk evaluations, which play important roles in the prevention and mitigation of highway disasters are very important. An evaluation model was established based on the superposition theory of regional influencing factors to highway flood disasters. Based on the formation mechanism and influencing factors of highway flood disasters, the main influencing factors were selected. These factors include rainstorms, terrain slopes, soil types, vegetation coverage and regional river density, which are based on evaluation indexes from climate conditions and underlying surface of the basin. A regional risk evaluation of highway flood disasters in Shaanxi was established using GIS. The risk index was divided into five levels using statistical methods, in accordance with the regional characteristics of highway flood disasters. Considering the difference in upfront investments, road grade, etc, between expressways and trunk-highways in China, a regional risk evaluation of trunk-highway flood disasters was completed. The evaluation results indicate that the risk evaluation is consistent with the actual situation. PMID:26528994

Regional Risk Evaluation of Flood Disasters for the Trunk-Highway in Shaanxi, China.

PubMed

Qi, Hong-Liang; Tian, Wei-Ping; Li, Jia-Chun

2015-10-29

Due to the complicated environment there are various types of highway disasters in Shaanxi Province (China). The damages caused are severe, losses are heavy, and have rapidly increased over the years, especially those caused by flood disasters along the rivers in mountainous areas. Therefore, research on risk evaluations, which play important roles in the prevention and mitigation of highway disasters are very important. An evaluation model was established based on the superposition theory of regional influencing factors to highway flood disasters. Based on the formation mechanism and influencing factors of highway flood disasters, the main influencing factors were selected. These factors include rainstorms, terrain slopes, soil types, vegetation coverage and regional river density, which are based on evaluation indexes from climate conditions and underlying surface of the basin. A regional risk evaluation of highway flood disasters in Shaanxi was established using GIS. The risk index was divided into five levels using statistical methods, in accordance with the regional characteristics of highway flood disasters. Considering the difference in upfront investments, road grade, etc, between expressways and trunk-highways in China, a regional risk evaluation of trunk-highway flood disasters was completed. The evaluation results indicate that the risk evaluation is consistent with the actual situation.

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.

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

Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe

PubMed Central

Guis, Helene; Caminade, Cyril; Calvete, Carlos; Morse, Andrew P.; Tran, Annelise; Baylis, Matthew

2012-01-01

Vector-borne diseases are among those most sensitive to climate because the ecology of vectors and the development rate of pathogens within them are highly dependent on environmental conditions. Bluetongue (BT), a recently emerged arboviral disease of ruminants in Europe, is often cited as an illustration of climate's impact on disease emergence, although no study has yet tested this association. Here, we develop a framework to quantitatively evaluate the effects of climate on BT's emergence in Europe by integrating high-resolution climate observations and model simulations within a mechanistic model of BT transmission risk. We demonstrate that a climate-driven model explains, in both space and time, many aspects of BT's recent emergence and spread, including the 2006 BT outbreak in northwest Europe which occurred in the year of highest projected risk since at least 1960. Furthermore, the model provides mechanistic insight into BT's emergence, suggesting that the drivers of emergence across Europe differ between the South and the North. Driven by simulated future climate from an ensemble of 11 regional climate models, the model projects increase in the future risk of BT emergence across most of Europe with uncertainty in rate but not in trend. The framework described here is adaptable and applicable to other diseases, where the link between climate and disease transmission risk can be quantified, permitting the evaluation of scale and uncertainty in climate change's impact on the future of such diseases. PMID:21697167

Risk assessment of brine contamination to aquatic resources from energy development in glacial drift deposits: Williston Basin, USA

USGS Publications Warehouse

Preston, Todd M.; Chesley-Preston, Tara

2015-01-01

Our goal was to improve the Sheridan County assessment (SCA) and evaluate the use of this new Williston Basin assessment (WBA) across 31 counties mantled by glacial drift in the Williston Basin. To determine if the WBA model improved the SCA model, results from both assessments were compared to CI values from 37 surface and groundwater samples collected to evaluate the SCA. The WBA (R2 = 0.65) outperformed the SCA (R2 = 0.52) indicating improved model performance. Applicability across the Williston Basin was evaluated by comparing WBA results to CI values from 123 surface water samples collected from 97 sections. Based on the WBA, the majority (83.5%) of sections lacked an oil well and had minimal risk. Sections with one or more oil wells comprised low (8.4%), moderate (6.5%), or high (1.7%) risk areas. The percentage of contaminated water samples, percentage of sections with at least one contaminated sample, and the average CI value of contaminated samples increased from low to high risk indicating applicability across the Williston Basin. Furthermore, the WBA performed better compared to only the contaminated samples (R2 = 0.62) versus all samples (R2 = 0.38). This demonstrates that the WBA was successful at identifying sections, but not individual aquatic resources, with an increased risk of contamination; therefore, WBA results can prioritize future sampling within areas of increased risk.

Evaluation of clinical risk factors for osteoporosis and applicability of the FRAX tool in Joinville City, Southern Brazil.

PubMed

Silva, Dalisbor Marcelo Weber; Borba, Victoria Zeghbi Cochenski; Kanis, John A

2017-12-09

Clinical risk factors for fracture in Southern Brazil are similar to those used in Fracture Risk Assessment Tool (FRAX®). Age-dependent intervention thresholds had higher accuracy than a fixed cut-off point. Access to bone mineral density testing is wanted for a large part of the Brazilian population. The FRAX® has an option to calculate the risk of fracture without this costly evaluation but relies on the clinical risk factors (CRFs) identified in the source cohorts used to generate FRAX. The aims of this study were to determine whether the CRFs used in FRAX are also risk indicators for individuals in Southern Brazil and to evaluate possible intervention thresholds for treatment in Brazil. We determined the CRFs for hip fractures in women and men aged 50 years and more with a hip fracture and controls in Joinville, Southern Brazil (April 1, 2010, and March 31, 2012). For intervention thresholds, we determined the accuracy of using the fixed thresholds of National Osteoporosis Foundation (NOF), USA, compared with the age-dependent thresholds of the National Osteoporosis Guideline Group (NOGG), UK. CRFs that were significant for hip fracture were very similar to FRAX, apart from chronic obstructive pulmonary disease and malabsorptive intestinal disease. FRAX based on the NOGG and NOF models had an accuracy of 64.2 and 58.7%, respectively. CRFs used in FRAX® were similar to those in the Southern Brazil. The NOGG model seems to be more accurate to discriminate patients with increased fracture risk in this population compared to the NOF model, but not significantly.

Anthropometric measures in cardiovascular disease prediction: comparison of laboratory-based versus non-laboratory-based model.

PubMed

Dhana, Klodian; Ikram, M Arfan; Hofman, Albert; Franco, Oscar H; Kavousi, Maryam

2015-03-01

Body mass index (BMI) has been used to simplify cardiovascular risk prediction models by substituting total cholesterol and high-density lipoprotein cholesterol. In the elderly, the ability of BMI as a predictor of cardiovascular disease (CVD) declines. We aimed to find the most predictive anthropometric measure for CVD risk to construct a non-laboratory-based model and to compare it with the model including laboratory measurements. The study included 2675 women and 1902 men aged 55-79 years from the prospective population-based Rotterdam Study. We used Cox proportional hazard regression analysis to evaluate the association of BMI, waist circumference, waist-to-hip ratio and a body shape index (ABSI) with CVD, including coronary heart disease and stroke. The performance of the laboratory-based and non-laboratory-based models was evaluated by studying the discrimination, calibration, correlation and risk agreement. Among men, ABSI was the most informative measure associated with CVD, therefore ABSI was used to construct the non-laboratory-based model. Discrimination of the non-laboratory-based model was not different than laboratory-based model (c-statistic: 0.680-vs-0.683, p=0.71); both models were well calibrated (15.3% observed CVD risk vs 16.9% and 17.0% predicted CVD risks by the non-laboratory-based and laboratory-based models, respectively) and Spearman rank correlation and the agreement between non-laboratory-based and laboratory-based models were 0.89 and 91.7%, respectively. Among women, none of the anthropometric measures were independently associated with CVD. Among middle-aged and elderly where the ability of BMI to predict CVD declines, the non-laboratory-based model, based on ABSI, could predict CVD risk as accurately as the laboratory-based model among men. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Proposal of a risk-factor-based analytical approach for integrating occupational health and safety into project risk evaluation.

PubMed

Badri, Adel; Nadeau, Sylvie; Gbodossou, André

2012-09-01

Excluding occupational health and safety (OHS) from project management is no longer acceptable. Numerous industrial accidents have exposed the ineffectiveness of conventional risk evaluation methods as well as negligence of risk factors having major impact on the health and safety of workers and nearby residents. Lack of reliable and complete evaluations from the beginning of a project generates bad decisions that could end up threatening the very existence of an organization. This article supports a systematic approach to the evaluation of OHS risks and proposes a new procedure based on the number of risk factors identified and their relative significance. A new concept called risk factor concentration along with weighting of risk factor categories as contributors to undesirable events are used in the analytical hierarchy process multi-criteria comparison model with Expert Choice(©) software. A case study is used to illustrate the various steps of the risk evaluation approach and the quick and simple integration of OHS at an early stage of a project. The approach allows continual reassessment of criteria over the course of the project or when new data are acquired. It was thus possible to differentiate the OHS risks from the risk of drop in quality in the case of the factory expansion project. Copyright © 2011 Elsevier Ltd. All rights reserved.

Improving the Impact and Implementation of Disaster Education: Programs for Children Through Theory-Based Evaluation.

PubMed

Johnson, Victoria A; Ronan, Kevin R; Johnston, David M; Peace, Robin

2016-11-01

A main weakness in the evaluation of disaster education programs for children is evaluators' propensity to judge program effectiveness based on changes in children's knowledge. Few studies have articulated an explicit program theory of how children's education would achieve desired outcomes and impacts related to disaster risk reduction in households and communities. This article describes the advantages of constructing program theory models for the purpose of evaluating disaster education programs for children. Following a review of some potential frameworks for program theory development, including the logic model, the program theory matrix, and the stage step model, the article provides working examples of these frameworks. The first example is the development of a program theory matrix used in an evaluation of ShakeOut, an earthquake drill practiced in two Washington State school districts. The model illustrates a theory of action; specifically, the effectiveness of school earthquake drills in preventing injuries and deaths during disasters. The second example is the development of a stage step model used for a process evaluation of What's the Plan Stan?, a voluntary teaching resource distributed to all New Zealand primary schools for curricular integration of disaster education. The model illustrates a theory of use; specifically, expanding the reach of disaster education for children through increased promotion of the resource. The process of developing the program theory models for the purpose of evaluation planning is discussed, as well as the advantages and shortcomings of the theory-based approaches. © 2015 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.

Risk assessment of hazardous release in air due to the chemical production of "P. Karaminchev" company in the town of Rousse.

PubMed

Diankova, M

1998-09-01

A health risk evaluation of the lifetime population risk has been made, by using the US EPA's method of risk assessment. Several main steps have been conducted: --a hazard identification, by means of emission analysis and mathematical modeling of air concentration dispersion; a dose-response evaluation and exposure assessment, and finally--a risk characterization. The health risk evaluation was conducted, using lifetime reference concentrations and doses. As risk descriptors were applied: --the individual exposure coefficient (IEC), the hazard quotient (HQ) and the margin of exposure (MOE)--for system toxicants, and the excess lifetime cancer risk (ELCR)--for carcinogens. The method that was used provides an upperbound estimate, including all possible exposures. The results showed, that the emissions of hydrogen chloride, phthalates (DOF), nitrogen oxides and most of the organic solvents, released from this chemical plant, are not a source of lifetime chronic health risk for the population of any of the six evaluated residential areas of Rousse. The rest of the hazardous emissions cause a slightly increased lifetime health risk, which is entirely in the so called 'controlled risk zone' the risk descriptors vary from 1.00 to 5.00. A number of actions have been prescribed to the plant's government, most of which were realized in the short term.

Computer simulation models of pre-diabetes populations: a systematic review protocol

PubMed Central

Khurshid, Waqar; Pagano, Eva; Feenstra, Talitha

2017-01-01

Introduction Diabetes is a major public health problem and prediabetes (intermediate hyperglycaemia) is associated with a high risk of developing diabetes. With evidence supporting the use of preventive interventions for prediabetes populations and the discovery of novel biomarkers stratifying the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. In diabetes and prediabetes, it is relevant to inform cost-effectiveness analysis using decision models due to their ability to forecast long-term health outcomes and costs beyond the time frame of clinical trials. To support good implementation and reimbursement decisions of interventions in these populations, models should be clinically credible, based on best available evidence, reproducible and validated against clinical data. Our aim is to identify recent studies on computer simulation models and model-based economic evaluations of populations of individuals with prediabetes, qualify them and discuss the knowledge gaps, challenges and opportunities that need to be addressed for future evaluations. Methods and analysis A systematic review will be conducted in MEDLINE, Embase, EconLit and National Health Service Economic Evaluation Database. We will extract peer-reviewed studies published between 2000 and 2016 that describe computer simulation models of the natural history of individuals with prediabetes and/or decision models to evaluate the impact of interventions, risk stratification and/or screening on these populations. Two reviewers will independently assess each study for inclusion. Data will be extracted using a predefined pro forma developed using best practice. Study quality will be assessed using a modelling checklist. A narrative synthesis of all studies will be presented, focussing on model structure, quality of models and input data, and validation status. Ethics and dissemination This systematic review is exempt from ethics approval because the work is carried out on published documents. The findings of the review will be disseminated in a related peer-reviewed journal and presented at conferences. Reviewregistration number CRD42016047228. PMID:28982807

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

PubMed

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

2018-04-20

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

Lifetime and 5 years risk of breast cancer and attributable risk factor according to Gail model in Iranian women

PubMed Central

Mohammadbeigi, Abolfazl; Mohammadsalehi, Narges; Valizadeh, Razieh; Momtaheni, Zeinab; Mokhtari, Mohsen; Ansari, Hossein

2015-01-01

Introduction: Breast cancer is the most commonly diagnosed cancers in women worldwide and in Iran. It is expected to account for 29% of all new cancers in women at 2015. This study aimed to assess the 5 years and lifetime risk of breast cancer according to Gail model, and to evaluate the effect of other additional risk factors on the Gail risk. Materials and Methods: A cross sectional study conducted on 296 women aged more than 34-year-old in Qom, Center of Iran. Breast Cancer Risk Assessment Tool calculated the Gail risk for each subject. Data were analyzed by paired t-test, independent t-test, and analysis of variance in bivariate approach to evaluate the effect of each factor on Gail risk. Multiple linear regression models with stepwise method were used to predict the effect of each variable on the Gail risk. Results: The mean age of the participants was 47.8 ± 8.8-year-old and 47% have Fars ethnicity. The 5 years and lifetime risk was 0.37 ± 0.18 and 4.48 ± 0.925%, respectively. It was lower than the average risk in same race and age women (P < 0.001). Being single, positive family history of breast cancer, positive history of biopsy, and radiotherapy as well as using nonhormonal contraceptives were related to higher lifetime risk (P < 0.05). Moreover, a significant direct correlation observed between lifetime risk and body mass index, age of first live birth, and menarche age. While an inversely correlation observed between lifetimes risk of breast cancer and total month of breast feeding duration and age. Conclusion: Based on our results, the 5 years and lifetime risk of breast cancer according to Gail model was lower than the same race and age. Moreover, by comparison with national epidemiologic indicators about morbidity and mortality of breast cancer, it seems that the Gail model overestimate the risk of breast cancer in Iranian women. PMID:26229355

Do Different Mental Models Influence Cybersecurity Behavior? Evaluations via Statistical Reasoning Performance.

PubMed

Brase, Gary L; Vasserman, Eugene Y; Hsu, William

2017-01-01

Cybersecurity research often describes people as understanding internet security in terms of metaphorical mental models (e.g., disease risk, physical security risk, or criminal behavior risk). However, little research has directly evaluated if this is an accurate or productive framework. To assess this question, two experiments asked participants to respond to a statistical reasoning task framed in one of four different contexts (cybersecurity, plus the above alternative models). Each context was also presented using either percentages or natural frequencies, and these tasks were followed by a behavioral likelihood rating. As in previous research, consistent use of natural frequencies promoted correct Bayesian reasoning. There was little indication, however, that any of the alternative mental models generated consistently better understanding or reasoning over the actual cybersecurity context. There was some evidence that different models had some effects on patterns of responses, including the behavioral likelihood ratings, but these effects were small, as compared to the effect of the numerical format manipulation. This points to a need to improve the content of actual internet security warnings, rather than working to change the models users have of warnings.

Do Different Mental Models Influence Cybersecurity Behavior? Evaluations via Statistical Reasoning Performance

PubMed Central

Brase, Gary L.; Vasserman, Eugene Y.; Hsu, William

2017-01-01

Cybersecurity research often describes people as understanding internet security in terms of metaphorical mental models (e.g., disease risk, physical security risk, or criminal behavior risk). However, little research has directly evaluated if this is an accurate or productive framework. To assess this question, two experiments asked participants to respond to a statistical reasoning task framed in one of four different contexts (cybersecurity, plus the above alternative models). Each context was also presented using either percentages or natural frequencies, and these tasks were followed by a behavioral likelihood rating. As in previous research, consistent use of natural frequencies promoted correct Bayesian reasoning. There was little indication, however, that any of the alternative mental models generated consistently better understanding or reasoning over the actual cybersecurity context. There was some evidence that different models had some effects on patterns of responses, including the behavioral likelihood ratings, but these effects were small, as compared to the effect of the numerical format manipulation. This points to a need to improve the content of actual internet security warnings, rather than working to change the models users have of warnings. PMID:29163304

Brownfields and health risks--air dispersion modeling and health risk assessment at landfill redevelopment sites.

PubMed

Ofungwu, Joseph; Eget, Steven

2006-07-01

Redevelopment of landfill sites in the New Jersey-New York metropolitan area for recreational (golf courses), commercial, and even residential purposes seems to be gaining acceptance among municipal planners and developers. Landfill gas generation, which includes methane and potentially toxic nonmethane compounds usually continues long after closure of the landfill exercise phase. It is therefore prudent to evaluate potential health risks associated with exposure to gas emissions before redevelopment of the landfill sites as recreational, commercial, and, especially, residential properties. Unacceptably high health risks would call for risk management measures such as limiting the development to commercial/recreational rather than residential uses, stringent gas control mechanisms, interior air filtration, etc. A methodology is presented for applying existing models to estimate residual landfill hazardous compounds emissions and to quantify associated health risks. Besides the toxic gas constituents of landfill emissions, other risk-related issues concerning buried waste, landfill leachate, and explosive gases were qualitatively evaluated. Five contiguously located landfill sites in New Jersey intended for residential and recreational redevelopment were used to exemplify the approach.

Measures of Residual Risk with Connections to Regression, Risk Tracking, Surrogate Models, and Ambiguity

DTIC Science & Technology

2015-05-20

original variable. Residual risk can be exempli ed as a quanti cation of the improved situation faced by a hedging investor compared to that of a...distributional information about Yx for every x as well as the computational cost of evaluating R(Yx) for numerous x, for example within an optimization...Still, when g is costly to evaluate , it might be desirable to develop an approximation of R(Yx), x ∈ IRn through regression based on observations {xj

A prognostic model based on readily available clinical data enriched a pre-emptive pharmacogenetic testing program.

PubMed

Schildcrout, Jonathan S; Shi, Yaping; Danciu, Ioana; Bowton, Erica; Field, Julie R; Pulley, Jill M; Basford, Melissa A; Gregg, William; Cowan, James D; Harrell, Frank E; Roden, Dan M; Peterson, Josh F; Denny, Joshua C

2016-04-01

We describe the development, implementation, and evaluation of a model to pre-emptively select patients for genotyping based on medication exposure risk. Using deidentified electronic health records, we derived a prognostic model for the prescription of statins, warfarin, or clopidogrel. The model was implemented into a clinical decision support (CDS) tool to recommend pre-emptive genotyping for patients exceeding a prescription risk threshold. We evaluated the rule on an independent validation cohort and on an implementation cohort, representing the population in which the CDS tool was deployed. The model exhibited moderate discrimination with area under the receiver operator characteristic curves ranging from 0.68 to 0.75 at 1 and 2 years after index dates. Risk estimates tended to underestimate true risk. The cumulative incidences of medication prescriptions at 1 and 2 years were 0.35 and 0.48, respectively, among 1,673 patients flagged by the model. The cumulative incidences in the same number of randomly sampled subjects were 0.12 and 0.19, and in patients over 50 years with the highest body mass indices, they were 0.22 and 0.34. We demonstrate that prognostic algorithms can guide pre-emptive pharmacogenetic testing toward those likely to benefit from it. Copyright © 2016 Elsevier Inc. All rights reserved.

[On risk-oriented model of sanitary epidemiologic surveillance in occupational hygiene].

PubMed

Zaitseval, N V; Mai, I V; Kostarev, V G; Bashketova, N S

2015-01-01

In 2015, Federal Service on surveillance in consumers rights protection and public well-being set a task to organize planned work of regional agencies on basis of risk-oriented model of control and supervision. Based on results of pilot project in Rospotrebnadzor Department of Perm area and St-Petersburg, the article covers methodic approaches to classification of objects liable to surveillance in occupational hygiene. The classification considers possibility of sanitary law violation, severity of this violation consequences and number of workers exposed to risk factors including hazardous work conditions. The authors specified recommendations on periodicity and forms of planned inspections considering evaluation of potential risk for human health, determined problems that require solution in implementation of risk-oriented model of surveillance.

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.

A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus.

PubMed

Sweeting, Arianne N; Wong, Jencia; Appelblom, Heidi; Ross, Glynis P; Kouru, Heikki; Williams, Paul F; Sairanen, Mikko; Hyett, Jon A

2018-06-13

Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11-13+6 weeks' gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks' gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89-0.94) overall, and 0.93 (95% CI 0.89-0.96) for early GDM, in a combined multivariate prediction model. A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM. © 2018 S. Karger AG, Basel.

Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran.

PubMed

Malekmohammadi, Bahram; Tayebzadeh Moghadam, Negar

2018-04-13

Environmental risk assessment (ERA) is a commonly used, effective tool applied to reduce adverse effects of environmental risk factors. In this study, ERA was investigated using the Bayesian network (BN) model based on a hierarchical structure of variables in an influence diagram (ID). ID facilitated ranking of the different alternatives under uncertainty that were then used to evaluate comparisons of the different risk factors. BN was used to present a new model for ERA applicable to complicated development projects such as dam construction. The methodology was applied to the Gabric Dam, in southern Iran. The main environmental risk factors in the region, presented by the Gabric Dam, were identified based on the Delphi technique and specific features of the study area. These included the following: flood, water pollution, earthquake, changes in land use, erosion and sedimentation, effects on the population, and ecosensitivity. These risk factors were then categorized based on results from the output decision node of the BN, including expected utility values for risk factors in the decision node. ERA was performed for the Gabric Dam using the analytical hierarchy process (AHP) method to compare results of BN modeling with those of conventional methods. Results determined that a BN-based hierarchical structure to ERA present acceptable and reasonable risk assessment prioritization in proposing suitable solutions to reduce environmental risks and can be used as a powerful decision support system for evaluating environmental risks.

Biomechanical, psychosocial and individual risk factors predicting low back functional impairment among furniture distribution employees

PubMed Central

Ferguson, Sue A.; Allread, W. Gary; Burr, Deborah L.; Heaney, Catherine; Marras, William S.

2013-01-01

Background Biomechanical, psychosocial and individual risk factors for low back disorder have been studied extensively however few researchers have examined all three risk factors. The objective of this was to develop a low back disorder risk model in furniture distribution workers using biomechanical, psychosocial and individual risk factors. Methods This was a prospective study with a six month follow-up time. There were 454 subjects at 9 furniture distribution facilities enrolled in the study. Biomechanical exposure was evaluated using the American Conference of Governmental Industrial Hygienists (2001) lifting threshold limit values for low back injury risk. Psychosocial and individual risk factors were evaluated via questionnaires. Low back health functional status was measured using the lumbar motion monitor. Low back disorder cases were defined as a loss of low back functional performance of −0.14 or more. Findings There were 92 cases of meaningful loss in low back functional performance and 185 non cases. A multivariate logistic regression model included baseline functional performance probability, facility, perceived workload, intermediated reach distance number of exertions above threshold limit values, job tenure manual material handling, and age combined to provide a model sensitivity of 68.5% and specificity of 71.9%. Interpretation: The results of this study indicate which biomechanical, individual and psychosocial risk factors are important as well as how much of each risk factor is too much resulting in increased risk of low back disorder among furniture distribution workers. PMID:21955915

Evaluations of Risks from the Lunar and Mars Radiation Environments

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee; Hayat, Matthew J.; Feiveson, Alan H.; Cucinotta, Francis A.

2008-01-01

Protecting astronauts from the space radiation environments requires accurate projections of radiation in future space missions. Characterization of the ionizing radiation environment is challenging because the interplanetary plasma and radiation fields are modulated by solar disturbances and the radiation doses received by astronauts in interplanetary space are likewise influenced. The galactic cosmic radiation (GCR) flux for the next solar cycle was estimated as a function of interplanetary deceleration potential, which has been derived from GCR flux and Climax neutron monitor rate measurements over the last 4 decades. For the chaotic nature of solar particle event (SPE) occurrence, the mean frequency of SPE at any given proton fluence threshold during a defined mission duration was obtained from a Poisson process model using proton fluence measurements of SPEs during the past 5 solar cycles (19-23). Analytic energy spectra of 34 historically large SPEs were constructed over broad energy ranges extending to GeV. Using an integrated space radiation model (which includes the transport codes HZETRN [1] and BRYNTRN [2], and the quantum nuclear interaction model QMSFRG[3]), the propagation and interaction properties of the energetic nucleons through various media were predicted. Risk assessment from GCR and SPE was evaluated at the specific organs inside a typical spacecraft using CAM [4] model. The representative risk level at each event size and their standard deviation were obtained from the analysis of 34 SPEs. Risks from different event sizes and their frequency of occurrences in a specified mission period were evaluated for the concern of acute health effects especially during extra-vehicular activities (EVA). The results will be useful for the development of an integrated strategy of optimizing radiation protection on the lunar and Mars missions. Keywords: Space Radiation Environments; Galactic Cosmic Radiation; Solar Particle Event; Radiation Risk; Risk Analysis; Radiation Protection.

The role of retinopathy distribution and other lesion types for the definition of examination intervals during screening for diabetic retinopathy.

PubMed

Ometto, Giovanni; Erlandsen, Mogens; Hunter, Andrew; Bek, Toke

2017-06-01

It has previously been shown that the intervals between screening examinations for diabetic retinopathy can be optimized by including individual risk factors for the development of the disease in the risk assessment. However, in some cases, the risk model calculating the screening interval may recommend a different interval than an experienced clinician. The purpose of this study was to evaluate the influence of factors unrelated to diabetic retinopathy and the distribution of lesions for discrepancies between decisions made by the clinician and the risk model. Therefore, fundus photographs from 90 screening examinations where the recommendations of the clinician and a risk model had been discrepant were evaluated. Forty features were defined to describe the type and location of the lesions, and classification and ranking techniques were used to assess whether the features could predict the discrepancy between the grader and the risk model. Suspicion of tumours, retinal degeneration and vascular diseases other than diabetic retinopathy could explain why the clinician recommended shorter examination intervals than the model. Additionally, the regional distribution of microaneurysms/dot haemorrhages was important for defining a photograph as belonging to the group where both the clinician and the risk model had recommended a short screening interval as opposed to the other decision alternatives. Features unrelated to diabetic retinopathy and the regional distribution of retinal lesions may affect the recommendation of the examination interval during screening for diabetic retinopathy. The development of automated computerized algorithms for extracting information about the type and location of retinal lesions could be expected to further optimize examination intervals during screening for diabetic retinopathy. © 2016 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

[Evaluation and prognosis of occupational risk in workers of nonferrous metallurgy enterprises].

PubMed

Shliapnikov, D M; Kostarev, V G

2014-01-01

The article deals with results of a priori and a posteriori evaluation of occupational risk for workers' health. Categories of a priori occupational risk for workers are estimated as high to very high (intolerable) risk. Findings are that work conditions in nonferrous metallurgy workshop result in upper respiratory tract diseases (medium degree of occupational conditionality). Increased prevalence of such diseases among the workers is connected with length of service. The authors revealed priority factors for occupationally conditioned diseases. A promising approach in occupational medicine is creation of methods to evaluate and forecast occupational risk, that enable to specify goal parameters for prophylactic measures. For example, modelling the risk of occupationally conditioned diseases via changes in exposure to occupational factor and length of service proved that decrease of chemical concentrations in air of workplace to maximally allowable ones lowers risk of respiratory diseases from 14 to 6 cases per year, for length of service of 5 years and population risk.

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...

Investigation on the Efficiency of Financial Companies in Malaysia with Data Envelopment Analysis Model

NASA Astrophysics Data System (ADS)

Weng Siew, Lam; Kah Fai, Liew; Weng Hoe, Lam

2018-04-01

Financial ratio and risk are important financial indicators to evaluate the financial performance or efficiency of the companies. Therefore, financial ratio and risk factor are needed to be taken into consideration to evaluate the efficiency of the companies with Data Envelopment Analysis (DEA) model. In DEA model, the efficiency of the company is measured as the ratio of sum-weighted outputs to sum-weighted inputs. The objective of this paper is to propose a DEA model by incorporating the financial ratio and risk factor in evaluating and comparing the efficiency of the financial companies in Malaysia. In this study, the listed financial companies in Malaysia from year 2004 until 2015 are investigated. The results of this study show that AFFIN, ALLIANZ, APEX, BURSA, HLCAP, HLFG, INSAS, LPI, MNRB, OSK, PBBANK, RCECAP and TA are ranked as efficient companies. This implies that these efficient companies have utilized their resources or inputs optimally to generate the maximum outputs. This study is significant because it helps to identify the efficient financial companies as well as determine the optimal input and output weights in maximizing the efficiency of financial companies in Malaysia.

Cost-oriented evaluation of ecosystem services under consideration of income risks and risk attitudes of farmers.

PubMed

Dörschner, T; Musshoff, O

2013-09-30

Agri-environmental measures are often not as accepted among farmers as is expected. The present study investigates whether changes in income risks and the individual risk attitudes of farmers may constitute an explanatory approach for the low acceptance of the measures. For this purpose, a normative model is developed that calculates the premia claimed by the farmers for adopting environmental measures under the consideration of income risks and different risk attitudes. We apply this model to environmental measures aiming at an increase of the faunistic diversity of species on grassland and showing that changes in income risks and the decision makers' risk attitudes can significantly influence farmers' minimum compensation claims. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Theoretical evaluation of the risk of decompression illness during simulated extravehicular activity].

PubMed

Nikolaev, V P

2008-01-01

Theoretical analysis of the risk of decompression illness (DI) during extravehicular activity following the Russian and NASA decompression protocols (D-R and D-US, respectively) was performed. In contrast to the tradition approach to decompression stress evaluation by the factor of tissue supersaturation with nitrogen, our probabilistic theory of decompression safety provides a completely reasoned evaluation and comparison of the levels of hazard of these decompression protocols. According to this theory, the function of cumulative DI risk is equal to the sum of functions of cumulative risk of lesion of all body tissues by gas bubbles and their supersaturation by solute gases. Based on modeling of dynamics of these functions, growth of the DI cumulative risk in the course of D-R and D-US follows essentially similar trajectories within the time-frame of up to 330 minutes. However, further extension of D-US but not D-R raises the risk of DI drastically.

A global airport-based risk model for the spread of dengue infection via the air transport network.

PubMed

Gardner, Lauren; Sarkar, Sahotra

2013-01-01

The number of travel-acquired dengue infections has seen a consistent global rise over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue has contributed to a rise in the number of dengue cases in both areas of endemicity and elsewhere. This paper reports results from a network-based risk assessment model which uses international passenger travel volumes, travel routes, travel distances, regional populations, and predictive species distribution models (for the two vector species, Aedes aegypti and Aedes albopictus) to quantify the relative risk posed by each airport in importing passengers with travel-acquired dengue infections. Two risk attributes are evaluated: (i) the risk posed by through traffic at each stopover airport and (ii) the risk posed by incoming travelers to each destination airport. The model results prioritize optimal locations (i.e., airports) for targeted dengue surveillance. The model is easily extendible to other vector-borne diseases.

A Global Airport-Based Risk Model for the Spread of Dengue Infection via the Air Transport Network

PubMed Central

Gardner, Lauren; Sarkar, Sahotra

2013-01-01

The number of travel-acquired dengue infections has seen a consistent global rise over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue has contributed to a rise in the number of dengue cases in both areas of endemicity and elsewhere. This paper reports results from a network-based risk assessment model which uses international passenger travel volumes, travel routes, travel distances, regional populations, and predictive species distribution models (for the two vector species, Aedes aegypti and Aedes albopictus) to quantify the relative risk posed by each airport in importing passengers with travel-acquired dengue infections. Two risk attributes are evaluated: (i) the risk posed by through traffic at each stopover airport and (ii) the risk posed by incoming travelers to each destination airport. The model results prioritize optimal locations (i.e., airports) for targeted dengue surveillance. The model is easily extendible to other vector-borne diseases. PMID:24009672

Risk of Mortality after Spinal Cord Injury: An 8-year Prospective Study

PubMed Central

Krause, James S.; Zhai, Yusheng; Saunders, Lee L.; Carter, Rickey E.

2011-01-01

Objective To evaluate a theoretical model for mortality after spinal cord injury (SCI) by sequentially analyzing 4 sets of risk factors in relation to mortality (i.e., adding 1 set of factors to the regression equation at a time). Design Prospective cohort study of data collected in late 1997 and early 1998 with mortality status ascertained in December 2005. We evaluated the significance of 4 successive sets of predictors (biographic and injury, psychologic and environmental, behavioral, health and secondary conditions) using Cox proportional hazards modeling and built a full model based on the optimal predictors. Setting A specialty hospital. Participants 1,386 adults with traumatic SCI, at least 1 year post-injury, participated. There were 224 deaths. After eliminating cases with missing data, there were 1,209 participants, with 179 deceased at follow-up. Interventions N/A. Main Outcome Measures Mortality status was determined using the National Death Index and the Social Security Death Index. Results The final model included one environmental variable (poverty), 2 behavioral factors (prescription medication use, binge drinking), and 4 health factors or secondary conditions (hospitalizations, fractures/amputations, surgeries for pressure ulcers, probable major depression). Conclusions The results supported the major premise of the theoretical model that risk factors are more important the more proximal they are in a theoretical chain of events leading to mortality. According to this model, mortality results from declining health, precipitated by high-risk behaviors. These findings may be used to target individuals who are at high risk for early mortality as well as directing interventions to the particular risk factor. PMID:19801060

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.

Diagnosis of Chronic Pancreatitis Incorporating Endosonographic Features, Demographics, and Behavioral Risk.

PubMed

Lee, Linda S; Tabak, Ying P; Kadiyala, Vivek; Sun, Xiaowu; Suleiman, Shadeah; Johannes, Richard S; Banks, Peter A; Conwell, Darwin L

2017-03-01

Diagnosing chronic pancreatitis remains challenging. Endoscopic ultrasound (EUS) is utilized to evaluate pancreatic disease. Abnormal pancreas function test is considered the "nonhistologic" criterion standard for chronic pancreatitis. We derived a prediction model for abnormal endoscopic pancreatic function test (ePFT) by enriching EUS findings with patient demographic and pancreatitis behavioral risk characteristics. Demographics, behavioral risk characteristics, EUS findings, and peak bicarbonate results were collected from patients evaluated for pancreatic disease. Abnormal ePFT was defined as peak bicarbonate of less than 75 mEq/L. We fit a logistic regression model and converted it to a risk score system. The risk score was validated using 1000 bootstrap simulations. A total of 176 patients were included; 61% were female with median age of 48 years (interquartile range, 38-57 years). Abnormal ePFT rate was 39.2% (69/176). Four variables formulated the risk score: alcohol or smoking status, number of parenchymal abnormalities, number of ductal abnormalities, and calcifications. Abnormal ePFT occurred in 10.7% with scores 4 or less versus 92.0% scoring 20 or greater. The model C-statistic was 0.78 (95% confidence interval, 0.71-0.85). Number of EUS pancreatic duct and parenchymal abnormalities, presence of calcification, and smoking/alcohol status were predictive of abnormal ePFT. This simple model has good discrimination for ePFT results.

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.

Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

PubMed

Li, Xiaohong; Blount, Patricia L; Vaughan, Thomas L; Reid, Brian J

2011-02-01

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.

A Risk Score Model for Evaluation and Management of Patients with Thyroid Nodules.

PubMed

Zhang, Yongwen; Meng, Fanrong; Hong, Lianqing; Chu, Lanfang

2018-06-12

The study is aimed to establish a simplified and practical tool for analyzing thyroid nodules. A novel risk score model was designed, risk factors including patient history, patient characteristics, physical examination, symptoms of compression, thyroid function, ultrasonography (US) of thyroid and cervical lymph nodes were evaluated and classified into high risk factors, intermediate risk factors, and low risk factors. A total of 243 thyroid nodules in 162 patients were assessed with risk score system and Thyroid Imaging-Reporting and Data System (TI-RADS). The diagnostic performance of risk score system and TI-RADS was compared. The accuracy in the diagnosis of thyroid nodules was 89.3% for risk score system, 74.9% for TI-RADS respectively. The specificity, accuracy and positive predictive value (PPV) of risk score system were significantly higher than the TI-RADS system (χ 2 =26.287, 17.151, 11.983; p <0.05), statistically significant differences were not observed in the sensitivity and negative predictive value (NPV) between the risk score system and TI-RADS (χ 2 =1.276, 0.290; p>0.05). The area under the curve (AUC) for risk score diagnosis system was 0.963, standard error 0.014, 95% confidence interval (CI)=0.934-0.991, the AUC for TI-RADS diagnosis system was 0.912 with standard error 0.021, 95% CI=0.871-0.953, the AUC for risk score system was significantly different from that of TI-RADS (Z=2.02; p <0.05). Risk score model is a reliable, simplified and cost-effective diagnostic tool used in diagnosis of thyroid cancer. The higher the score is, the higher the risk of malignancy will be. © Georg Thieme Verlag KG Stuttgart · New York.

Geographic Mapping as a Tool for Identifying Communities at High Risk for Fires.

PubMed

Fahey, Erin; Lehna, Carlee; Hanchette, Carol; Coty, Mary-Beth

2016-01-01

The purpose of this study was to evaluate whether the sample of older adults in a home fire safety (HFS) study captured participants living in the areas at highest risk for fire occurrence. The secondary aim was to identify high risk areas to focus future HFS interventions. Geographic information systems software was used to identify census tracts where study participants resided. Census data for these tracts were compared with participant data based on seven risk factors (ie, age greater than 65 years, nonwhite race, below high school education, low socioeconomic status, rented housing, year home built, home value) previously identified in a fire risk model. The distribution of participants and census tracts among risk categories determined how well higher risk census tracts were sampled. Of the 46 census tracts where the HFS intervention was implemented, 78% (n = 36) were identified as high or severe risk according to the fire risk model. Study participants' means for median annual family income (P < .0001) and median home value (P < .0001) were significantly lower than the census tract means (n = 46), indicating participants were at higher risk of fire occurrence. Of the 92 census tracts identified as high or severe risk in the entire county, the study intervention was implemented in 39% (n = 36), indicating 56 census tracts as potential areas for future HFS interventions. The Geographic information system-based fire risk model is an underutilized but important tool for practice that allows community agencies to develop, plan, and evaluate their outreach efforts and ensure the most effective use of scarce resources.

Evaluation of a model of violence risk assessment among forensic psychiatric patients.

PubMed

Douglas, Kevin S; Ogloff, James R P; Hart, Stephen D

2003-10-01

This study tested the interrater reliability and criterion-related validity of structured violence risk judgments made by using one application of the structured professional judgment model of violence risk assessment, the HCR-20 violence risk assessment scheme, which assesses 20 key risk factors in three domains: historical, clinical, and risk management. The HCR-20 was completed for a sample of 100 forensic psychiatric patients who had been found not guilty by reason of a mental disorder and were subsequently released to the community. Violence in the community was determined from multiple file-based sources. Interrater reliability of structured final risk judgments of low, moderate, or high violence risk made on the basis of the structured professional judgment model was acceptable (weighted kappa=.61). Structured final risk judgments were significantly predictive of postrelease community violence, yielding moderate to large effect sizes. Event history analyses showed that final risk judgments made with the structured professional judgment model added incremental validity to the HCR-20 used in an actuarial (numerical) sense. The findings support the structured professional judgment model of risk assessment as well as the HCR-20 specifically and suggest that clinical judgment, if made within a structured context, can contribute in meaningful ways to the assessment of violence risk.

Diagnosis-Based Risk Adjustment for Medicare Capitation Payments

PubMed Central

Ellis, Randall P.; Pope, Gregory C.; Iezzoni, Lisa I.; Ayanian, John Z.; Bates, David W.; Burstin, Helen; Ash, Arlene S.

1996-01-01

Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula. PMID:10172666

Probabilistic Methods for Structural Reliability and Risk

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2010-01-01

A probabilistic method is used to evaluate the structural reliability and risk of select metallic and composite structures. The method is a multiscale, multifunctional and it is based on the most elemental level. A multifactor interaction model is used to describe the material properties which are subsequently evaluated probabilistically. The metallic structure is a two rotor aircraft engine, while the composite structures consist of laminated plies (multiscale) and the properties of each ply are the multifunctional representation. The structural component is modeled by finite element. The solution method for structural responses is obtained by an updated simulation scheme. The results show that the risk for the two rotor engine is about 0.0001 and the composite built-up structure is also 0.0001.

Probabilistic Methods for Structural Reliability and Risk

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A probabilistic method is used to evaluate the structural reliability and risk of select metallic and composite structures. The method is a multiscale, multifunctional and it is based on the most elemental level. A multi-factor interaction model is used to describe the material properties which are subsequently evaluated probabilistically. The metallic structure is a two rotor aircraft engine, while the composite structures consist of laminated plies (multiscale) and the properties of each ply are the multifunctional representation. The structural component is modeled by finite element. The solution method for structural responses is obtained by an updated simulation scheme. The results show that the risk for the two rotor engine is about 0.0001 and the composite built-up structure is also 0.0001.

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.

Forecasting Tehran stock exchange volatility; Markov switching GARCH approach

NASA Astrophysics Data System (ADS)

Abounoori, Esmaiel; Elmi, Zahra (Mila); Nademi, Younes

2016-03-01

This paper evaluates several GARCH models regarding their ability to forecast volatility in Tehran Stock Exchange (TSE). These include GARCH models with both Gaussian and fat-tailed residual conditional distribution, concerning their ability to describe and forecast volatility from 1-day to 22-day horizon. Results indicate that AR(2)-MRSGARCH-GED model outperforms other models at one-day horizon. Also, the AR(2)-MRSGARCH-GED as well as AR(2)-MRSGARCH-t models outperform other models at 5-day horizon. In 10 day horizon, three models of AR(2)-MRSGARCH outperform other models. Concerning 22 day forecast horizon, results indicate no differences between MRSGARCH models with that of standard GARCH models. Regarding Risk management out-of-sample evaluation (95% VaR), a few models seem to provide reasonable and accurate VaR estimates at 1-day horizon, with a coverage rate close to the nominal level. According to the risk management loss functions, there is not a uniformly most accurate model.

[Evaluation of a training system for middle ear surgery with optoelectric detection].

PubMed

Strauss, G; Bahrami, N; Pössneck, A; Strauss, M; Dietz, A; Korb, W; Lüth, T; Haase, R; Moeckel, H; Grunert, R

2009-10-01

This work presents a new training concept for surgery of the temporal bone. It is based on a model of gypsum plastic with optoelectric detection of risk structures. A prototypical evaluation is given. The training models are based on high-resolution computed tomographic data of a human skull. The resulting data set was printed by a three-dimensional (3D) printer. A 3D phantom is created from gypsum powder and a bonding agent. Risks structures are the facial nerve, semicircular canal, cochlea, ossicular chain, sigmoid sinus, dura, and internal carotid artery. An electrically conductive metal (Wood's metal) and a fiber-optic cable were used as detection materials for the risk structures. For evaluating the training system, a study was done with eight inexperienced and eight experienced ear surgeons. They were asked to perform temporal bone surgery using two identical training models (group A). In group B, the same surgeons underwent surgical training with human cadavers. In the case of injuries, the number, point in time, degree (facial nerve), and injured structure were documented during the training on the model. In addition, the total time needed was noted. The training systems could be used in all cases. Evaluation of the anatomic accuracy of the models showed results that were between 49.5% and 90% agreement with the anatomic origin. Error detection was evaluated with values between 79% and 100% agreement with the perception of an experienced surgeon. The operating setting was estimated to be better than the previous"gold standard." The possibility of completely replacing the previous training method, which uses cadavers, with the examined training model was affirmed. This study shows that the examined system fulfills the conditions for a new training concept for temporal bone surgery. The system connects the preliminary work with printed and sintered models with the possibilities of microsystem engineering. In addition, the model's digital database permits a complete virtual representation of the model with appropriate further applications ("look behind the wall," virtual endoscopy).

Risk Assessment of Groundwater Contamination: A Multilevel Fuzzy Comprehensive Evaluation Approach Based on DRASTIC Model

PubMed Central

Zhang, Yan; Zhong, Ming

2013-01-01

Groundwater contamination is a serious threat to water supply. Risk assessment of groundwater contamination is an effective way to protect the safety of groundwater resource. Groundwater is a complex and fuzzy system with many uncertainties, which is impacted by different geological and hydrological factors. In order to deal with the uncertainty in the risk assessment of groundwater contamination, we propose an approach with analysis hierarchy process and fuzzy comprehensive evaluation integrated together. Firstly, the risk factors of groundwater contamination are identified by the sources-pathway-receptor-consequence method, and a corresponding index system of risk assessment based on DRASTIC model is established. Due to the complexity in the process of transitions between the possible pollution risks and the uncertainties of factors, the method of analysis hierarchy process is applied to determine the weights of each factor, and the fuzzy sets theory is adopted to calculate the membership degrees of each factor. Finally, a case study is presented to illustrate and test this methodology. It is concluded that the proposed approach integrates the advantages of both analysis hierarchy process and fuzzy comprehensive evaluation, which provides a more flexible and reliable way to deal with the linguistic uncertainty and mechanism uncertainty in groundwater contamination without losing important information. PMID:24453883

Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

PubMed

Akbarzadeh Baghban, Alireza; Pourhoseingholi, Asma; Zayeri, Farid; Jafari, Ali Akbar; Alavian, Seyed Moayed

2013-01-01

In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history.

PubMed

Sparks, Jeffrey A; Chen, Chia-Yen; Jiang, Xia; Askling, Johan; Hiraki, Linda T; Malspeis, Susan; Klareskog, Lars; Alfredsson, Lars; Costenbader, Karen H; Karlson, Elizabeth W

2015-08-01

To develop and validate rheumatoid arthritis (RA) risk models based on family history, epidemiologic factors and known genetic risk factors. We developed and validated models for RA based on known RA risk factors, among women in two cohorts: the Nurses' Health Study (NHS, 381 RA cases and 410 controls) and the Epidemiological Investigation of RA (EIRA, 1244 RA cases and 971 controls). Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC) in logistic regression models for the study population and for those with positive family history. The joint effect of family history with genetics, smoking and body mass index (BMI) was evaluated using logistic regression models to estimate ORs for RA. The complete model including family history, epidemiologic risk factors and genetics demonstrated AUCs of 0.74 for seropositive RA in NHS and 0.77 for anti-citrullinated protein antibody (ACPA)-positive RA in EIRA. Among women with positive family history, discrimination was excellent for complete models for seropositive RA in NHS (AUC 0.82) and ACPA-positive RA in EIRA (AUC 0.83). Positive family history, high genetic susceptibility, smoking and increased BMI had an OR of 21.73 for ACPA-positive RA. We developed models for seropositive and seronegative RA phenotypes based on family history, epidemiological and genetic factors. Among those with positive family history, models using epidemiologic and genetic factors were highly discriminatory for seropositive and seronegative RA. Assessing epidemiological and genetic factors among those with positive family history may identify individuals suitable for RA prevention strategies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Estimating risks of heat strain by age and sex: a population-level simulation model.

PubMed

Glass, Kathryn; Tait, Peter W; Hanna, Elizabeth G; Dear, Keith

2015-05-18

Individuals living in hot climates face health risks from hyperthermia due to excessive heat. Heat strain is influenced by weather exposure and by individual characteristics such as age, sex, body size, and occupation. To explore the population-level drivers of heat strain, we developed a simulation model that scales up individual risks of heat storage (estimated using Myrup and Morgan's man model "MANMO") to a large population. Using Australian weather data, we identify high-risk weather conditions together with individual characteristics that increase the risk of heat stress under these conditions. The model identifies elevated risks in children and the elderly, with females aged 75 and older those most likely to experience heat strain. Risk of heat strain in males does not increase as rapidly with age, but is greatest on hot days with high solar radiation. Although cloudy days are less dangerous for the wider population, older women still have an elevated risk of heat strain on hot cloudy days or when indoors during high temperatures. Simulation models provide a valuable method for exploring population level risks of heat strain, and a tool for evaluating public health and other government policy interventions.

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

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

PubMed Central

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

2009-01-01

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

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

Gutierrez, Marte

Colorado School of Mines conducted research and training in the development and validation of an advanced CO{sub 2} GS (Geological Sequestration) probabilistic simulation and risk assessment model. CO{sub 2} GS simulation and risk assessment is used to develop advanced numerical simulation models of the subsurface to forecast CO2 behavior and transport; optimize site operational practices; ensure site safety; and refine site monitoring, verification, and accounting efforts. As simulation models are refined with new data, the uncertainty surrounding the identified risks decrease, thereby providing more accurate risk assessment. The models considered the full coupling of multiple physical processes (geomechanical and fluidmore » flow) and describe the effects of stochastic hydro-mechanical (H-M) parameters on the modeling of CO{sub 2} flow and transport in fractured porous rocks. Graduate students were involved in the development and validation of the model that can be used to predict the fate, movement, and storage of CO{sub 2} in subsurface formations, and to evaluate the risk of potential leakage to the atmosphere and underground aquifers. The main major contributions from the project include the development of: 1) an improved procedure to rigorously couple the simulations of hydro-thermomechanical (H-M) processes involved in CO{sub 2} GS; 2) models for the hydro-mechanical behavior of fractured porous rocks with random fracture patterns; and 3) probabilistic methods to account for the effects of stochastic fluid flow and geomechanical properties on flow, transport, storage and leakage associated with CO{sub 2} GS. The research project provided the means to educate and train graduate students in the science and technology of CO{sub 2} GS, with a focus on geologic storage. Specifically, the training included the investigation of an advanced CO{sub 2} GS simulation and risk assessment model that can be used to predict the fate, movement, and storage of CO{sub 2} in underground formations, and the evaluation of the risk of potential CO{sub 2} leakage to the atmosphere and underground aquifers.« 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.

Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

PubMed

Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

2014-06-26

To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

Branch-and-Bound algorithm applied to uncertainty quantification of a Boiling Water Reactor Station Blackout

DOE PAGES

Nielsen, Joseph; Tokuhiro, Akira; Hiromoto, Robert; ...

2015-11-13

Evaluation of the impacts of uncertainty and sensitivity in modeling presents a significant set of challenges in particular to high fidelity modeling. Computational costs and validation of models creates a need for cost effective decision making with regards to experiment design. Experiments designed to validate computation models can be used to reduce uncertainty in the physical model. In some cases, large uncertainty in a particular aspect of the model may or may not have a large impact on the final results. For example, modeling of a relief valve may result in large uncertainty, however, the actual effects on final peakmore » clad temperature in a reactor transient may be small and the large uncertainty with respect to valve modeling may be considered acceptable. Additionally, the ability to determine the adequacy of a model and the validation supporting it should be considered within a risk informed framework. Low fidelity modeling with large uncertainty may be considered adequate if the uncertainty is considered acceptable with respect to risk. In other words, models that are used to evaluate the probability of failure should be evaluated more rigorously with the intent of increasing safety margin. Probabilistic risk assessment (PRA) techniques have traditionally been used to identify accident conditions and transients. Traditional classical event tree methods utilize analysts’ knowledge and experience to identify the important timing of events in coordination with thermal-hydraulic modeling. These methods lack the capability to evaluate complex dynamic systems. In these systems, time and energy scales associated with transient events may vary as a function of transition times and energies to arrive at a different physical state. Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. Unfortunately DPRA methods introduce issues associated with combinatorial explosion of states. This study presents a methodology to address combinatorial explosion using a Branch-and-Bound algorithm applied to Dynamic Event Trees (DET), which utilize LENDIT (L – Length, E – Energy, N – Number, D – Distribution, I – Information, and T – Time) as well as a set theory to describe system, state, resource, and response (S2R2) sets to create bounding functions for the DET. The optimization of the DET in identifying high probability failure branches is extended to create a Phenomenological Identification and Ranking Table (PIRT) methodology to evaluate modeling parameters important to safety of those failure branches that have a high probability of failure. The PIRT can then be used as a tool to identify and evaluate the need for experimental validation of models that have the potential to reduce risk. Finally, in order to demonstrate this methodology, a Boiling Water Reactor (BWR) Station Blackout (SBO) case study is presented.« less

A risk assessment tool applied to the study of shale gas resources.

PubMed

Veiguela, Miguel; Hurtado, Antonio; Eguilior, Sonsoles; Recreo, Fernando; Roqueñi, Nieves; Loredo, Jorge

2016-11-15

The implementation of a risk assessment tool with the capacity to evaluate the risks for health, safety and the environment (HSE) from extraction of non-conventional fossil fuel resources by the hydraulic fracturing (fracking) technique can be a useful tool to boost development and progress of the technology and winning public trust and acceptance of this. At the early project stages, the lack of data related the selection of non-conventional gas deposits makes it difficult the use of existing approaches to risk assessment of fluids injected into geologic formations. The qualitative risk assessment tool developed in this work is based on the approach that shale gas exploitation risk is dependent on both the geologic site and the technological aspects. It follows from the Oldenburg's 'Screening and Ranking Framework (SRF)' developed to evaluate potential geologic carbon dioxide (CO2) storage sites. These two global characteristics: (1) characteristics centered on the natural aspects of the site and (2) characteristics centered on the technological aspects of the Project, have been evaluated through user input of Property values, which define Attributes, which define the Characteristics. In order to carry out an individual evaluation of each of the characteristics and the elements of the model, the tool has been implemented in a spreadsheet. The proposed model has been applied to a site with potential for the exploitation of shale gas in Asturias (northwestern Spain) with tree different technological options to test the approach. Copyright © 2016 Elsevier B.V. All rights reserved.

A case study to illustrate the utility of the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks for integrating human health and ecological data into cumulative risk assessment

EPA Science Inventory

Cumulative risk assessment (CRA) methods, which evaluate the risk of multiple adverse outcomes (AOs) from multiple chemicals, promote the use of a conceptual site model (CSM) to integrate risk from relevant stressors. The Adverse Outcome Pathway (AOP) framework can inform these r...

Assessing natural hazards in forestry for risk management: a review

Treesearch

Marc Hanewinkel; Susan Hummel; Axel Albrecht

2011-01-01

We address the problem of how to integrate risk assessment into forest management and therefore provide a comprehensive review of recent and past literature on risk analysis and modeling and, moreover, an evaluation and summary on these papers. We provide a general scheme on how to integrate concepts of risk into forest management decisions. After an overview of the...

Unmanned aircraft system sense and avoid integrity and continuity

NASA Astrophysics Data System (ADS)

Jamoom, Michael B.

This thesis describes new methods to guarantee safety of sense and avoid (SAA) functions for Unmanned Aircraft Systems (UAS) by evaluating integrity and continuity risks. Previous SAA efforts focused on relative safety metrics, such as risk ratios, comparing the risk of using an SAA system versus not using it. The methods in this thesis evaluate integrity and continuity risks as absolute measures of safety, as is the established practice in commercial aircraft terminal area navigation applications. The main contribution of this thesis is a derivation of a new method, based on a standard intruder relative constant velocity assumption, that uses hazard state estimates and estimate error covariances to establish (1) the integrity risk of the SAA system not detecting imminent loss of '"well clear," which is the time and distance required to maintain safe separation from intruder aircraft, and (2) the probability of false alert, the continuity risk. Another contribution is applying these integrity and continuity risk evaluation methods to set quantifiable and certifiable safety requirements on sensors. A sensitivity analysis uses this methodology to evaluate the impact of sensor errors on integrity and continuity risks. The penultimate contribution is an integrity and continuity risk evaluation where the estimation model is refined to address realistic intruder relative linear accelerations, which goes beyond the current constant velocity standard. The final contribution is an integrity and continuity risk evaluation addressing multiple intruders. This evaluation is a new innovation-based method to determine the risk of mis-associating intruder measurements. A mis-association occurs when the SAA system incorrectly associates a measurement to the wrong intruder, causing large errors in the estimated intruder trajectories. The new methods described in this thesis can help ensure safe encounters between aircraft and enable SAA sensor certification for UAS integration into the National Airspace System.

Attributional style as a mediator between parental abuse risk and child internalizing symptomatology.

PubMed

Rodriguez, Christina M

2006-05-01

This study examined a model wherein children's attributional style mediates the relationship between parental physical child-abuse risk and children's internalizing problems. Using structural equation modeling, three indices of abuse risk were selected (child abuse potential, physical discipline use, and dysfunctional parenting style) and two indices of children's internalizing problems (depression and anxiety). The sample included 75 parent-child dyads, in which parents reported on their abuse risk and children independently completed measures of depressive and anxious symptomatology and a measure on their attributional style. Findings supported the model that children's attributional style for positive events (but not negative events) partially mediated the relationship between abuse risk and internalizing symptoms, with significant direct and indirect effects of abuse risk on internalizing symptomatology. Future directions to continue evaluating additional mediators and other possible contextual variables are discussed.

Evaluation of Cost Leadership Strategy in Shipping Enterprises with Simulation Model

NASA Astrophysics Data System (ADS)

Ferfeli, Maria V.; Vaxevanou, Anthi Z.; Damianos, Sakas P.

2009-08-01

The present study will attempt the evaluation of cost leadership strategy that prevails in certain shipping enterprises and the creation of simulation models based on strategic model STAIR. The above model is an alternative method of strategic applications evaluation. This is held in order to be realised if the strategy of cost leadership creates competitive advantage [1] and this will be achieved via the technical simulation which appreciates the interactions between the operations of an enterprise and the decision-making strategy in conditions of uncertainty with reduction of undertaken risk.

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer.

PubMed

Wu, Lang; Shi, Wei; Long, Jirong; Guo, Xingyi; Michailidou, Kyriaki; Beesley, Jonathan; Bolla, Manjeet K; Shu, Xiao-Ou; Lu, Yingchang; Cai, Qiuyin; Al-Ejeh, Fares; Rozali, Esdy; Wang, Qin; Dennis, Joe; Li, Bingshan; Zeng, Chenjie; Feng, Helian; Gusev, Alexander; Barfield, Richard T; Andrulis, Irene L; Anton-Culver, Hoda; Arndt, Volker; Aronson, Kristan J; Auer, Paul L; Barrdahl, Myrto; Baynes, Caroline; Beckmann, Matthias W; Benitez, Javier; Bermisheva, Marina; Blomqvist, Carl; Bogdanova, Natalia V; Bojesen, Stig E; Brauch, Hiltrud; Brenner, Hermann; Brinton, Louise; Broberg, Per; Brucker, Sara Y; Burwinkel, Barbara; Caldés, Trinidad; Canzian, Federico; Carter, Brian D; Castelao, J Esteban; Chang-Claude, Jenny; Chen, Xiaoqing; Cheng, Ting-Yuan David; Christiansen, Hans; Clarke, Christine L; Collée, Margriet; Cornelissen, Sten; Couch, Fergus J; Cox, David; Cox, Angela; Cross, Simon S; Cunningham, Julie M; Czene, Kamila; Daly, Mary B; Devilee, Peter; Doheny, Kimberly F; Dörk, Thilo; Dos-Santos-Silva, Isabel; Dumont, Martine; Dwek, Miriam; Eccles, Diana M; Eilber, Ursula; Eliassen, A Heather; Engel, Christoph; Eriksson, Mikael; Fachal, Laura; Fasching, Peter A; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fritschi, Lin; Gabrielson, Marike; Gago-Dominguez, Manuela; Gapstur, Susan M; García-Closas, Montserrat; Gaudet, Mia M; Ghoussaini, Maya; Giles, Graham G; Goldberg, Mark S; Goldgar, David E; González-Neira, Anna; Guénel, Pascal; Hahnen, Eric; Haiman, Christopher A; Håkansson, Niclas; Hall, Per; Hallberg, Emily; Hamann, Ute; Harrington, Patricia; Hein, Alexander; Hicks, Belynda; Hillemanns, Peter; Hollestelle, Antoinette; Hoover, Robert N; Hopper, John L; Huang, Guanmengqian; Humphreys, Keith; Hunter, David J; Jakubowska, Anna; Janni, Wolfgang; John, Esther M; Johnson, Nichola; Jones, Kristine; Jones, Michael E; Jung, Audrey; Kaaks, Rudolf; Kerin, Michael J; Khusnutdinova, Elza; Kosma, Veli-Matti; Kristensen, Vessela N; Lambrechts, Diether; Le Marchand, Loic; Li, Jingmei; Lindström, Sara; Lissowska, Jolanta; Lo, Wing-Yee; Loibl, Sibylle; Lubinski, Jan; Luccarini, Craig; Lux, Michael P; MacInnis, Robert J; Maishman, Tom; Kostovska, Ivana Maleva; Mannermaa, Arto; Manson, JoAnn E; Margolin, Sara; Mavroudis, Dimitrios; Meijers-Heijboer, Hanne; Meindl, Alfons; Menon, Usha; Meyer, Jeffery; Mulligan, Anna Marie; Neuhausen, Susan L; Nevanlinna, Heli; Neven, Patrick; Nielsen, Sune F; Nordestgaard, Børge G; Olopade, Olufunmilayo I; Olson, Janet E; Olsson, Håkan; Peterlongo, Paolo; Peto, Julian; Plaseska-Karanfilska, Dijana; Prentice, Ross; Presneau, Nadege; Pylkäs, Katri; Rack, Brigitte; Radice, Paolo; Rahman, Nazneen; Rennert, Gad; Rennert, Hedy S; Rhenius, Valerie; Romero, Atocha; Romm, Jane; Rudolph, Anja; Saloustros, Emmanouil; Sandler, Dale P; Sawyer, Elinor J; Schmidt, Marjanka K; Schmutzler, Rita K; Schneeweiss, Andreas; Scott, Rodney J; Scott, Christopher G; Seal, Sheila; Shah, Mitul; Shrubsole, Martha J; Smeets, Ann; Southey, Melissa C; Spinelli, John J; Stone, Jennifer; Surowy, Harald; Swerdlow, Anthony J; Tamimi, Rulla M; Tapper, William; Taylor, Jack A; Terry, Mary Beth; Tessier, Daniel C; Thomas, Abigail; Thöne, Kathrin; Tollenaar, Rob A E M; Torres, Diana; Truong, Thérèse; Untch, Michael; Vachon, Celine; Van Den Berg, David; Vincent, Daniel; Waisfisz, Quinten; Weinberg, Clarice R; Wendt, Camilla; Whittemore, Alice S; Wildiers, Hans; Willett, Walter C; Winqvist, Robert; Wolk, Alicja; Xia, Lucy; Yang, Xiaohong R; Ziogas, Argyrios; Ziv, Elad; Dunning, Alison M; Pharoah, Paul D P; Simard, Jacques; Milne, Roger L; Edwards, Stacey L; Kraft, Peter; Easton, Douglas F; Chenevix-Trench, Georgia; Zheng, Wei

2018-06-18

The breast cancer risk variants identified in genome-wide association studies explain only a small fraction of the familial relative risk, and the genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82 × 10 -6 , including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony-forming efficiency. Our study provides new insights into breast cancer genetics and biology.

A Deterministic Model to Quantify Risk and Guide Mitigation Strategies to Reduce Bluetongue Virus Transmission in California Dairy Cattle

PubMed Central

Mayo, Christie; Shelley, Courtney; MacLachlan, N. James; Gardner, Ian; Hartley, David; Barker, Christopher

2016-01-01

The global distribution of bluetongue virus (BTV) has been changing recently, perhaps as a result of climate change. To evaluate the risk of BTV infection and transmission in a BTV-endemic region of California, sentinel dairy cows were evaluated for BTV infection, and populations of Culicoides vectors were collected at different sites using carbon dioxide. A deterministic model was developed to quantify risk and guide future mitigation strategies to reduce BTV infection in California dairy cattle. The greatest risk of BTV transmission was predicted within the warm Central Valley of California that contains the highest density of dairy cattle in the United States. Temperature and parameters associated with Culicoides vectors (transmission probabilities, carrying capacity, and survivorship) had the greatest effect on BTV’s basic reproduction number, R0. Based on these analyses, optimal control strategies for reducing BTV infection risk in dairy cattle will be highly reliant upon early efforts to reduce vector abundance during the months prior to peak transmission. PMID:27812161

Risk Evaluation for Identification and Intervention in Dual Use Research of Concern (DURC) for International Biological R&D Activity

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

Jones, Katherine A.; DeMenno, Mercy; Hoffman, Matthew John

This report summarizes the work performed as part of a Laboratory Directed Research and Development project focused on evaluating and mitigating risk associated with biological dual use research of concern. The academic and scientific community has identified the funding stage as the appropriate place to intervene and mitigate risk, so the framework developed here uses a portfolio-level approach and balances biosafety and biosecurity risks, anticipated project benefits, and available mitigations to identify the best available investment strategies subject to cost constraints. The modeling toolkit was designed for decision analysis for dual use research of concern, but is flexible enough tomore » support a wide variety of portfolio-level funding decisions where risk/benefit tradeoffs are involved. Two mathematical optimization models with two solution methods are included to accommodate stakeholders with varying levels of certainty about priorities between metrics. An example case study is presented.« less

Assaying the Effect of Levodopa on the Evaluation of Risk in Healthy Humans

PubMed Central

Symmonds, Mkael; Wright, Nicholas D.; Fagan, Elizabeth; Dolan, Raymond J.

2013-01-01

In humans, dopamine is implicated in reward and risk-based decision-making. However, the specific effects of dopamine augmentation on risk evaluation are unclear. Here we sought to measure the effect of 100 mg oral levodopa, which enhances synaptic release of dopamine, on choice behaviour in healthy humans. We use a paradigm without feedback or learning, which solely isolates effects on risk evaluation. We present two studies (n = 20; n = 20) employing a randomised, placebo-controlled, within-subjects design. We manipulated different dimensions of risk in a controlled economic paradigm. We test effects on risk-reward tradeoffs, assaying both aversion to variance (the spread of possible outcomes) and preference for relative losses and gains (asymmetry of outcomes - skewness), dissociating this from potential non-specific effects on choice randomness using behavioural modelling. There were no systematic effects of levodopa on risk attitudes, either for variance or skewness. However, there was a drift towards more risk-averse behaviour over time, indicating that this paradigm was sensitive to detect changes in risk-preferences. These findings suggest that levodopa administration does not change the evaluation of risk. One possible reason is that dopaminergic influences on decision making may be due to changing the response to reward feedback. PMID:23844168

Development and evaluation of a risk communication curriculum for medical students.

PubMed

Han, Paul K J; Joekes, Katherine; Elwyn, Glyn; Mazor, Kathleen M; Thomson, Richard; Sedgwick, Philip; Ibison, Judith; Wong, John B

2014-01-01

To develop, pilot, and evaluate a curriculum for teaching clinical risk communication skills to medical students. A new experience-based curriculum, "Risk Talk," was developed and piloted over a 1-year period among students at Tufts University School of Medicine. An experimental study of 2nd-year students exposed vs. unexposed to the curriculum was conducted to evaluate the curriculum's efficacy. Primary outcome measures were students' objective (observed) and subjective (self-reported) risk communication competence; the latter was assessed using an Observed Structured Clinical Examination (OSCE) employing new measures. Twenty-eight 2nd-year students completed the curriculum, and exhibited significantly greater (p<.001) objective and subjective risk communication competence than a convenience sample of 24 unexposed students. New observational measures of objective competence in risk communication showed promising evidence of reliability and validity. The curriculum was resource-intensive. The new experience-based clinical risk communication curriculum was efficacious, although resource-intensive. More work is needed to develop the feasibility of curriculum delivery, and to improve the measurement of competence in clinical risk communication. Risk communication is an important advanced communication skill, and the Risk Talk curriculum provides a model educational intervention and new assessment tools to guide future efforts to teach and evaluate this skill. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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.

Applications of Living Fire PRA models to Fire Protection Significance Determination Process in Taiwan

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

De-Cheng, Chen; Chung-Kung, Lo; Tsu-Jen, Lin

2004-07-01

The living fire probabilistic risk assessment (PRA) models for all three operating nuclear power plants (NPPs) in Taiwan had been established in December 2000. In that study, a scenario-based PRA approach was adopted to systematically evaluate the fire and smoke hazards and associated risks. Using these fire PRA models developed, a risk-informed application project had also been completed in December 2002 for the evaluation of cable-tray fire-barrier wrapping exemption. This paper presents a new application of the fire PRA models to fire protection issues using the fire protection significance determination process (FP SDP). The fire protection issues studied may involvemore » the selection of appropriate compensatory measures during the period when an automatic fire detection or suppression system in a safety-related fire zone becomes inoperable. The compensatory measure can either be a 24-hour fire watch or an hourly fire patrol. The living fire PRA models were used to estimate the increase in risk associated with the fire protection issue in terms of changes in core damage frequency (CDF) and large early release frequency (LERF). In compliance with SDP at-power and the acceptance guidelines specified in RG 1.174, the fire protection issues in question can be grouped into four categories; red, yellow, white and green, in accordance with the guidelines developed for FD SDP. A 24-hour fire watch is suggested only required for the yellow condition, while an hourly fire patrol may be adopted for the white condition. More limiting requirement is suggested for the red condition, but no special consideration is needed for the green condition. For the calculation of risk measures, risk impacts from any additional fire scenarios that may have been introduced, as well as more severe initiating events and fire damages that may accompany the fire protection issue should be considered carefully. Examples are presented in this paper to illustrate the evaluation process. (authors)« less

Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers.

PubMed

Kerr, Kathleen F; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

2014-08-07

The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients' risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. Copyright © 2014 by the American Society of Nephrology.

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.

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

PubMed

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

2013-04-01

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

Program Evaluation of Growin' to Win: A Latchkey and Summer Program for At-Risk Youth.

ERIC Educational Resources Information Center

James, William H.; And Others

This document presents an evaluation of the effectiveness of the Growin' to Win Project, an after-school and summer program targeted at elementary and middle school aged youth at high risk of substance abuse and gang involvement. Growin' to Win is an expansion of a model latchkey program piloted at two Tacoma (Washington) schools in 1990. The…

A new risk prediction model for critical care: the Intensive Care National Audit & Research Centre (ICNARC) model.

PubMed

Harrison, David A; Parry, Gareth J; Carpenter, James R; Short, Alasdair; Rowan, Kathy

2007-04-01

To develop a new model to improve risk prediction for admissions to adult critical care units in the UK. Prospective cohort study. The setting was 163 adult, general critical care units in England, Wales, and Northern Ireland, December 1995 to August 2003. Patients were 216,626 critical care admissions. None. The performance of different approaches to modeling physiologic measurements was evaluated, and the best methods were selected to produce a new physiology score. This physiology score was combined with other information relating to the critical care admission-age, diagnostic category, source of admission, and cardiopulmonary resuscitation before admission-to develop a risk prediction model. Modeling interactions between diagnostic category and physiology score enabled the inclusion of groups of admissions that are frequently excluded from risk prediction models. The new model showed good discrimination (mean c index 0.870) and fit (mean Shapiro's R 0.665, mean Brier's score 0.132) in 200 repeated validation samples and performed well when compared with recalibrated versions of existing published risk prediction models in the cohort of patients eligible for all models. The hypothesis of perfect fit was rejected for all models, including the Intensive Care National Audit & Research Centre (ICNARC) model, as is to be expected in such a large cohort. The ICNARC model demonstrated better discrimination and overall fit than existing risk prediction models, even following recalibration of these models. We recommend it be used to replace previously published models for risk adjustment in the UK.

Empirical evaluation of spatial and non-spatial European-scale multimedia fate models: results and implications for chemical risk assessment.

PubMed

Armitage, James M; Cousins, Ian T; Hauck, Mara; Harbers, Jasper V; Huijbregts, Mark A J

2007-06-01

Multimedia environmental fate models are commonly-applied tools for assessing the fate and distribution of contaminants in the environment. Owing to the large number of chemicals in use and the paucity of monitoring data, such models are often adopted as part of decision-support systems for chemical risk assessment. The purpose of this study was to evaluate the performance of three multimedia environmental fate models (spatially- and non-spatially-explicit) at a European scale. The assessment was conducted for four polycyclic aromatic hydrocarbons (PAHs) and hexachlorobenzene (HCB) and compared predicted and median observed concentrations using monitoring data collected for air, water, sediments and soils. Model performance in the air compartment was reasonable for all models included in the evaluation exercise as predicted concentrations were typically within a factor of 3 of the median observed concentrations. Furthermore, there was good correspondence between predictions and observations in regions that had elevated median observed concentrations for both spatially-explicit models. On the other hand, all three models consistently underestimated median observed concentrations in sediment and soil by 1-3 orders of magnitude. Although regions with elevated median observed concentrations in these environmental media were broadly identified by the spatially-explicit models, the magnitude of the discrepancy between predicted and median observed concentrations is of concern in the context of chemical risk assessment. These results were discussed in terms of factors influencing model performance such as the steady-state assumption, inaccuracies in emission estimates and the representativeness of monitoring data.

Evaluation of the APEX model to simulate runoff quality from agricultural fields in the southern region of the US

USDA-ARS?s Scientific Manuscript database

The phosphorus (P) Index (PI) is the risk assessment tool approved in the NRCS 590 standard used to target critical source areas and practices to reduce P losses. A revision of the 590 standard, suggested using the Agricultural Policy/Environmental eXtender (APEX) model to assess the risk of nitroge...

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

PubMed

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

2018-03-01

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

Relevance of the c-statistic when evaluating risk-adjustment models in surgery.

PubMed

Merkow, Ryan P; Hall, Bruce L; Cohen, Mark E; Dimick, Justin B; Wang, Edward; Chow, Warren B; Ko, Clifford Y; Bilimoria, Karl Y

2012-05-01

The measurement of hospital quality based on outcomes requires risk adjustment. The c-statistic is a popular tool used to judge model performance, but can be limited, particularly when evaluating specific operations in focused populations. Our objectives were to examine the interpretation and relevance of the c-statistic when used in models with increasingly similar case mix and to consider an alternative perspective on model calibration based on a graphical depiction of model fit. From the American College of Surgeons National Surgical Quality Improvement Program (2008-2009), patients were identified who underwent a general surgery procedure, and procedure groups were increasingly restricted: colorectal-all, colorectal-elective cases only, and colorectal-elective cancer cases only. Mortality and serious morbidity outcomes were evaluated using logistic regression-based risk adjustment, and model c-statistics and calibration curves were used to compare model performance. During the study period, 323,427 general, 47,605 colorectal-all, 39,860 colorectal-elective, and 21,680 colorectal cancer patients were studied. Mortality ranged from 1.0% in general surgery to 4.1% in the colorectal-all group, and serious morbidity ranged from 3.9% in general surgery to 12.4% in the colorectal-all procedural group. As case mix was restricted, c-statistics progressively declined from the general to the colorectal cancer surgery cohorts for both mortality and serious morbidity (mortality: 0.949 to 0.866; serious morbidity: 0.861 to 0.668). Calibration was evaluated graphically by examining predicted vs observed number of events over risk deciles. For both mortality and serious morbidity, there was no qualitative difference in calibration identified between the procedure groups. In the present study, we demonstrate how the c-statistic can become less informative and, in certain circumstances, can lead to incorrect model-based conclusions, as case mix is restricted and patients become more homogenous. Although it remains an important tool, caution is advised when the c-statistic is advanced as the sole measure of a model performance. Copyright © 2012 American College of Surgeons. All rights reserved.

Socio-Ecological Risk Factors for Prime-Age Adult Death in Two Coastal Areas of Vietnam

PubMed Central

Kim, Deok Ryun; Ali, Mohammad; Thiem, Vu Dinh; Wierzba, Thomas F.

2014-01-01

Background Hierarchical spatial models enable the geographic and ecological analysis of health data thereby providing useful information for designing effective health interventions. In this study, we used a Bayesian hierarchical spatial model to evaluate mortality data in Vietnam. The model enabled identification of socio-ecological risk factors and generation of risk maps to better understand the causes and geographic implications of prime-age (15 to less than 45 years) adult death. Methods and Findings The study was conducted in two sites: Nha Trang and Hue in Vietnam. The study areas were split into 500×500 meter cells to define neighborhoods. We first extracted socio-demographic data from population databases of the two sites, and then aggregated the data by neighborhood. We used spatial hierarchical model that borrows strength from neighbors for evaluating risk factors and for creating spatially smoothed risk map after adjusting for neighborhood level covariates. The Markov chain Monte Carlo procedure was used to estimate the parameters. Male mortality was more than twice the female mortality. The rates also varied by age and sex. The most frequent cause of mortality was traffic accidents and drowning for men and traffic accidents and suicide for women. Lower education of household heads in the neighborhood was an important risk factor for increased mortality. The mortality was highly variable in space and the socio-ecological risk factors are sensitive to study site and sex. Conclusion Our study suggests that lower education of the household head is an important predictor for prime age adult mortality. Variability in socio-ecological risk factors and in risk areas by sex make it challenging to design appropriate intervention strategies aimed at decreasing prime-age adult deaths in Vietnam. PMID:24587031

Socio-ecological risk factors for prime-age adult death in two coastal areas of Vietnam.

PubMed

Kim, Deok Ryun; Ali, Mohammad; Thiem, Vu Dinh; Wierzba, Thomas F

2014-01-01

Hierarchical spatial models enable the geographic and ecological analysis of health data thereby providing useful information for designing effective health interventions. In this study, we used a Bayesian hierarchical spatial model to evaluate mortality data in Vietnam. The model enabled identification of socio-ecological risk factors and generation of risk maps to better understand the causes and geographic implications of prime-age (15 to less than 45 years) adult death. The study was conducted in two sites: Nha Trang and Hue in Vietnam. The study areas were split into 500×500 meter cells to define neighborhoods. We first extracted socio-demographic data from population databases of the two sites, and then aggregated the data by neighborhood. We used spatial hierarchical model that borrows strength from neighbors for evaluating risk factors and for creating spatially smoothed risk map after adjusting for neighborhood level covariates. The Markov chain Monte Carlo procedure was used to estimate the parameters. Male mortality was more than twice the female mortality. The rates also varied by age and sex. The most frequent cause of mortality was traffic accidents and drowning for men and traffic accidents and suicide for women. Lower education of household heads in the neighborhood was an important risk factor for increased mortality. The mortality was highly variable in space and the socio-ecological risk factors are sensitive to study site and sex. Our study suggests that lower education of the household head is an important predictor for prime age adult mortality. Variability in socio-ecological risk factors and in risk areas by sex make it challenging to design appropriate intervention strategies aimed at decreasing prime-age adult deaths in Vietnam.

Comparison of the Mortality Probability Admission Model III, National Quality Forum, and Acute Physiology and Chronic Health Evaluation IV hospital mortality models: implications for national benchmarking*.

PubMed

Kramer, Andrew A; Higgins, Thomas L; Zimmerman, Jack E

2014-03-01

To examine the accuracy of the original Mortality Probability Admission Model III, ICU Outcomes Model/National Quality Forum modification of Mortality Probability Admission Model III, and Acute Physiology and Chronic Health Evaluation IVa models for comparing observed and risk-adjusted hospital mortality predictions. Retrospective paired analyses of day 1 hospital mortality predictions using three prognostic models. Fifty-five ICUs at 38 U.S. hospitals from January 2008 to December 2012. Among 174,001 intensive care admissions, 109,926 met model inclusion criteria and 55,304 had data for mortality prediction using all three models. None. We compared patient exclusions and the discrimination, calibration, and accuracy for each model. Acute Physiology and Chronic Health Evaluation IVa excluded 10.7% of all patients, ICU Outcomes Model/National Quality Forum 20.1%, and Mortality Probability Admission Model III 24.1%. Discrimination of Acute Physiology and Chronic Health Evaluation IVa was superior with area under receiver operating curve (0.88) compared with Mortality Probability Admission Model III (0.81) and ICU Outcomes Model/National Quality Forum (0.80). Acute Physiology and Chronic Health Evaluation IVa was better calibrated (lowest Hosmer-Lemeshow statistic). The accuracy of Acute Physiology and Chronic Health Evaluation IVa was superior (adjusted Brier score = 31.0%) to that for Mortality Probability Admission Model III (16.1%) and ICU Outcomes Model/National Quality Forum (17.8%). Compared with observed mortality, Acute Physiology and Chronic Health Evaluation IVa overpredicted mortality by 1.5% and Mortality Probability Admission Model III by 3.1%; ICU Outcomes Model/National Quality Forum underpredicted mortality by 1.2%. Calibration curves showed that Acute Physiology and Chronic Health Evaluation performed well over the entire risk range, unlike the Mortality Probability Admission Model and ICU Outcomes Model/National Quality Forum models. Acute Physiology and Chronic Health Evaluation IVa had better accuracy within patient subgroups and for specific admission diagnoses. Acute Physiology and Chronic Health Evaluation IVa offered the best discrimination and calibration on a large common dataset and excluded fewer patients than Mortality Probability Admission Model III or ICU Outcomes Model/National Quality Forum. The choice of ICU performance benchmarks should be based on a comparison of model accuracy using data for identical patients.

Targeted and Non-Targeted Analysis of Serum Pools to Provide Chemical Exposure Data for Exposure Modeling and Chemical Prioritization

EPA Science Inventory

Biomonitoring data can help inform the development and calibration of high-throughput exposure modeling for use in prioritization and risk evaluation. A pilot project was conducted to evaluate the feasibility of using pooled banked blood samples to generate initial data on popul...

Quantifying Children's Aggregate (Dietary and Residential) Exposure and Dose to Permethin: Application and Evaluation of EPA's Probabilistic SHED-Multimedia Model

EPA Science Inventory

Reliable, evaluated human exposure and dose models are important for understanding the health risks from chemicals. A case study focusing on permethrin was conducted because of this insecticide’s widespread use and potential health effects. SHEDS-Multimedia was applied to estimat...

Collaboratively Evaluating Cooperative Extension Educational Interventions.

ERIC Educational Resources Information Center

Webb, Debb; Murphy, Dennis J.; Kiernan, Nancy Ellen

2001-01-01

Three intervention models to reduce hazards and risks of farm work were tested: self-audit (n=73), youth safety and health program (n=64), and a community coalition for safety and health (n=17). Despite some difficulties, university researchers and agents did accomplish the primary goal: scientific evaluation of models of safety education. (SK)

Evaluation of Smoking Prevention Television Messages Based on the Elaboration Likelihood Model

ERIC Educational Resources Information Center

Flynn, Brian S.; Worden, John K.; Bunn, Janice Yanushka; Connolly, Scott W.; Dorwaldt, Anne L.

2011-01-01

Progress in reducing youth smoking may depend on developing improved methods to communicate with higher risk youth. This study explored the potential of smoking prevention messages based on the Elaboration Likelihood Model (ELM) to address these needs. Structured evaluations of 12 smoking prevention messages based on three strategies derived from…

Evaluating a Training Intervention for Assessing Nonsuicidal Self-Injury: The HIRE Model

ERIC Educational Resources Information Center

Rutt, Corrine C.; Buser, Trevor J.; Buser, Juleen K.

2016-01-01

The authors evaluated the effectiveness of a brief training intervention with graduate counseling students who used the HIRE (history, interest in change, reasons for engaging in the behavior, and exposure to risk; Buser & Buser, 2013b) model for the informal assessment of nonsuicidal self-injury. The intervention group demonstrated…

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

PubMed

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

2017-01-01

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

A Prognostic Indicator for Patients Hospitalized with Heart Failure.

PubMed

Snow, Richard; Vogel, Karen; Vanderhoff, Bruce; Kelch, Benjamin P; Ferris, Frank D

2016-12-01

Current methods for identifying patients at risk of dying within six months suffer from clinician biases resulting in underestimation of this risk. As a result, patients who are potentially eligible for hospice and palliative care services frequently do not benefit from these services until they are very close to the end of their lives. To develop a prospective prognostic indicator based on actual survival within Centers for Medicare and Medicaid Services (CMS) claims data that identifies patients with congestive heart failure (CHF) who are at risk of six-month mortality. CMS claims data from January 1, 2008 to June 30, 2009 were reviewed to find the first hospitalization for CHF patients with episode of care diagnosis-related groups (DRGs) 291, 292, and 293. Univariate and multivariable analyses were used to determine the associations between demographic and clinical factors and six-month mortality. The resulting model was evaluated for discrimination and calibration. The resulting prospective prognostic model demonstrated fair discrimination with an ROC of 0.71 and good calibration with a Hosmer-Lemshow statistic of 0.98. Across all DRGs, 5% of discharged patients had a six-month mortality risk of greater than 50%. This prospective approach appears to provide a method to identify patients with CHF who would potentially benefit from a clinical evaluation for referral to hospice care or for a palliative care consult due to high predicted risk of dying within 180 days after discharge from a hospital. This approach can provide a model to match at-risk patients with evidenced-based care in a more consistent manner. This method of identifying patients at risk needs further prospective evaluation to see if it has value for clinicians, increases referrals to hospice and palliative care services, and benefits patients and families.

Evaluation of Non-Laboratory and Laboratory Prediction Models for Current and Future Diabetes Mellitus: A Cross-Sectional and Retrospective Cohort Study

PubMed Central

Hahn, Seokyung; Moon, Min Kyong; Park, Kyong Soo; Cho, Young Min

2016-01-01

Background Various diabetes risk scores composed of non-laboratory parameters have been developed, but only a few studies performed cross-validation of these scores and a comparison with laboratory parameters. We evaluated the performance of diabetes risk scores composed of non-laboratory parameters, including a recently published Korean risk score (KRS), and compared them with laboratory parameters. Methods The data of 26,675 individuals who visited the Seoul National University Hospital Healthcare System Gangnam Center for a health screening program were reviewed for cross-sectional validation. The data of 3,029 individuals with a mean of 6.2 years of follow-up were reviewed for longitudinal validation. The KRS and 16 other risk scores were evaluated and compared with a laboratory prediction model developed by logistic regression analysis. Results For the screening of undiagnosed diabetes, the KRS exhibited a sensitivity of 81%, a specificity of 58%, and an area under the receiver operating characteristic curve (AROC) of 0.754. Other scores showed AROCs that ranged from 0.697 to 0.782. For the prediction of future diabetes, the KRS exhibited a sensitivity of 74%, a specificity of 54%, and an AROC of 0.696. Other scores had AROCs ranging from 0.630 to 0.721. The laboratory prediction model composed of fasting plasma glucose and hemoglobin A1c levels showed a significantly higher AROC (0.838, P < 0.001) than the KRS. The addition of the KRS to the laboratory prediction model increased the AROC (0.849, P = 0.016) without a significant improvement in the risk classification (net reclassification index: 4.6%, P = 0.264). Conclusions The non-laboratory risk scores, including KRS, are useful to estimate the risk of undiagnosed diabetes but are inferior to the laboratory parameters for predicting future diabetes. PMID:27214034

Benzodiazepines and antipsychotic medications for treatment of acute cocaine toxicity in animal models--a systematic review and meta-analysis.

PubMed

Heard, Kennon; Cleveland, Nathan R; Krier, Shay

2011-11-01

There are no controlled human studies to determine the efficacy of benzodiazepines or antipsychotic medications for prevention or treatment of acute cocaine toxicity. The only available controlled data are from animal models and these studies have reported inconsistent benefits. The objective of this study was to quantify the reported efficacy of benzodiazepines and antipsychotic medication for the prevention of mortality due to cocaine poisoning. We conducted a systematic review to identify English language articles describing experiments that compared a benzodiazepine or antipsychotic medication to placebo for the prevention of acute cocaine toxicity in an animal model. We then used these articles in a meta-analysis with a random-effects model to quantify the absolute risk reduction observed in these experiments. We found 10 articles evaluating antipsychotic medications and 15 articles evaluating benzodiazepines. Antipsychotic medications reduced the risk of death by 27% (95% CI, 15.2%-38.7%) compared to placebo and benzodiazepines reduced the risk of death by 52% (42.8%-60.7%) compared to placebo. Both treatments showed evidence of a dose-response effect, and no experiment found a statistically significant increase in risk of death. We conclude that both benzodiazepines and antipsychotic medications are effective for the prevention of lethality from cocaine toxicity in animal models.

EVALUATION OF VADOSE ZONE AND SORUCE MODULES FOR MULTI-MEDIA, MULTI-PATHWAY, AND MULTI-RECEPTOR RISK ASSESSMENT USING LARGE-SOIL-COLUMN EXPERIMENTAL DATA

EPA Science Inventory

The United States 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 sof...

75 FR 71397 - Regulations Establishing and Governing the Duties of Swap Dealers and Major Swap Participants

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-23

... risks associated with the use of models to derive market valuations or otherwise calculate or evaluate... swap dealers and major swap participants registered with the Commission with regard to: Risk management...) \\3\\ to establish a comprehensive regulatory framework to reduce risk, increase transparency, and...

DEVELOPMENT OF LANDSCAPE INDICATORS FOR USE IN REGIONAL ECOLOGICAL RISK ASSESSMENTS

EPA Science Inventory

There is a growing need for cost effective ways to assess conditions of and risks to ecological resources at a variety of scales over broad regions. Indicators, models and assessment tools are needed to evaluate water bodies at risk to non-point source pollution and to be able t...

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.

Cascade reservoir flood control operation based on risk grading and warning in the Upper Yellow River

NASA Astrophysics Data System (ADS)

Xuejiao, M.; Chang, J.; Wang, Y.

2017-12-01

Flood risk reduction with non-engineering measures has become the main idea for flood management. It is more effective for flood risk management to take various non-engineering measures. In this paper, a flood control operation model for cascade reservoirs in the Upper Yellow River was proposed to lower the flood risk of the water system with multi-reservoir by combining the reservoir flood control operation (RFCO) and flood early warning together. Specifically, a discharge control chart was employed to build the joint RFCO simulation model for cascade reservoirs in the Upper Yellow River. And entropy-weighted fuzzy comprehensive evaluation method was adopted to establish a multi-factorial risk assessment model for flood warning grade. Furthermore, after determining the implementing mode of countermeasures with future inflow, an intelligent optimization algorithm was used to solve the optimization model for applicable water release scheme. In addition, another model without any countermeasure was set to be a comparative experiment. The results show that the model developed in this paper can further decrease the flood risk of water system with cascade reservoirs. It provides a new approach to flood risk management by coupling flood control operation and flood early warning of cascade reservoirs.

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.

Geographic Profiling to Assess the Risk of Rare Plant Poaching in Natural Areas

NASA Astrophysics Data System (ADS)

Young, John A.; van Manen, Frank T.; Thatcher, Cindy A.

2011-09-01

We demonstrate the use of an expert-assisted spatial model to examine geographic factors influencing the poaching risk of a rare plant (American ginseng, Panax quinquefolius L.) in Shenandoah National Park, Virginia, USA. Following principles of the analytic hierarchy process (AHP), we identified a hierarchy of 11 geographic factors deemed important to poaching risk and requested law enforcement personnel of the National Park Service to rank those factors in a series of pair-wise comparisons. We used those comparisons to determine statistical weightings of each factor and combined them into a spatial model predicting poaching risk. We tested the model using 69 locations of previous poaching incidents recorded by law enforcement personnel. These locations occurred more frequently in areas predicted by the model to have a higher risk of poaching than random locations. The results of our study can be used to evaluate resource protection strategies and to target law enforcement activities.

Using random forest for the risk assessment of coal-floor water inrush in Panjiayao Coal Mine, northern China

NASA Astrophysics Data System (ADS)

Zhao, Dekang; Wu, Qiang; Cui, Fangpeng; Xu, Hua; Zeng, Yifan; Cao, Yufei; Du, Yuanze

2018-04-01

Coal-floor water-inrush incidents account for a large proportion of coal mine disasters in northern China, and accurate risk assessment is crucial for safe coal production. A novel and promising assessment model for water inrush is proposed based on random forest (RF), which is a powerful intelligent machine-learning algorithm. RF has considerable advantages, including high classification accuracy and the capability to evaluate the importance of variables; in particularly, it is robust in dealing with the complicated and non-linear problems inherent in risk assessment. In this study, the proposed model is applied to Panjiayao Coal Mine, northern China. Eight factors were selected as evaluation indices according to systematic analysis of the geological conditions and a field survey of the study area. Risk assessment maps were generated based on RF, and the probabilistic neural network (PNN) model was also used for risk assessment as a comparison. The results demonstrate that the two methods are consistent in the risk assessment of water inrush at the mine, and RF shows a better performance compared to PNN with an overall accuracy higher by 6.67%. It is concluded that RF is more practicable to assess the water-inrush risk than PNN. The presented method will be helpful in avoiding water inrush and also can be extended to various engineering applications.

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

PubMed

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

2017-12-15

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

Development of a relative risk model for evaluating ecological risk of water environment in the Haihe River Basin estuary area.

PubMed

Chen, Qiuying; Liu, Jingling; Ho, Kin Chung; Yang, Zhifeng

2012-03-15

Ecological risk assessment for water environment is significant to water resource management of basin. Effective environmental management and systems restoration such as the Haihe River Basin require holistic understanding of the relative importance of various stressor-related impacts throughout the basin. As an effective technical tool for evaluating the ecological risk, relative risk model (RRM) was applied in regional scale successfully. In this study, the risk transfer from upstream of basin was considered and the RRM was developed through introducing the source-stressor-habitat exposure filter (SSH), the endpoint-habitat exposure filter (EH) and the stressor-endpoint effect filter (SE) to reflect the meaning of exposure and effect more explicit. Water environment which includes water quality, water quantity and aquatic ecosystems was selected as the assessment endpoints. We created a conceptual model which depicting potential and effect pathways from source to stressor to habitat to endpoint. The Haihe River Basin estuary (HRBE) was selected as the model case. The results showed that there were two low risk regions, one medium risk region and two high risk regions in the HRBE. The results also indicated that urbanization was the biggest source, the second was shipping and the third was industry, their risk scores are 5.65, 4.71 and 3.68 respectively. Furthermore, habitat destruction was the largest stressor with the risk scores (2.66), the second was oxygen consuming organic pollutants (1.75) and the third was pathogens (1.75). So these three stressors were the main influencing factors of the ecological pressure in the study area. For habitats, open waters (9.59) and intertidal mudflat were enduring the bigger pressure and should be taken considerable attention. Ecological service values damaged (30.54) and biodiversity decreased were facing the biggest risk pressure. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

Semi-automated landform classification for hazard mapping of soil liquefaction by earthquake

NASA Astrophysics Data System (ADS)

Nakano, Takayuki

2018-05-01

Soil liquefaction damages were caused by huge earthquake in Japan, and the similar damages are concerned in near future huge earthquake. On the other hand, a preparation of soil liquefaction risk map (soil liquefaction hazard map) is impeded by the difficulty of evaluation of soil liquefaction risk. Generally, relative soil liquefaction risk should be able to be evaluated from landform classification data by using experimental rule based on the relationship between extent of soil liquefaction damage and landform classification items associated with past earthquake. Therefore, I rearranged the relationship between landform classification items and soil liquefaction risk intelligibly in order to enable the evaluation of soil liquefaction risk based on landform classification data appropriately and efficiently. And I developed a new method of generating landform classification data of 50-m grid size from existing landform classification data of 250-m grid size by using digital elevation model (DEM) data and multi-band satellite image data in order to evaluate soil liquefaction risk in detail spatially. It is expected that the products of this study contribute to efficient producing of soil liquefaction hazard map by local government.

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

PubMed

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

2012-09-01

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

Mechanistic modeling of insecticide risks to breeding birds in ...

EPA Pesticide Factsheets

Insecticide usage in the United States is ubiquitous in urban, suburban, and rural environments. In evaluating data for an insecticide registration application and for registration review, scientists at the United States Environmental Protection Agency (USEPA) assess the fate of the insecticide and the risk the insecticide poses to the environment and non-target wildlife. At the present time, current USEPA risk assessments do not include population-level endpoints. In this paper, we present a new mechanistic model, which allows risk assessors to estimate the effects of insecticide exposure on the survival and seasonal productivity of birds known to use agricultural fields during their breeding season. The new model was created from two existing USEPA avian risk assessment models, the Terrestrial Investigation Model (TIM v.3.0) and the Markov Chain Nest Productivity model (MCnest). The integrated TIM/MCnest model has been applied to assess the relative risk of 12 insecticides used to control corn pests on a suite of 31 avian species known to use cornfields in midwestern agroecosystems. The 12 insecticides that were assessed in this study are all used to treat major pests of corn (corn root worm borer, cutworm, and armyworm). After running the integrated TIM/MCnest model, we found extensive differences in risk to birds among insecticides, with chlorpyrifos and malathion (organophosphates) generally posing the greatest risk, and bifenthrin and ë-cyhalothrin (

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.

A decision model to predict the risk of the first fall onset.

PubMed

Deschamps, Thibault; Le Goff, Camille G; Berrut, Gilles; Cornu, Christophe; Mignardot, Jean-Baptiste

2016-08-01

Miscellaneous features from various domains are accepted to be associated with the risk of falling in the elderly. However, only few studies have focused on establishing clinical tools to predict the risk of the first fall onset. A model that would objectively and easily evaluate the risk of a first fall occurrence in the coming year still needs to be built. We developed a model based on machine learning, which might help the medical staff predict the risk of the first fall onset in a one-year time window. Overall, 426 older adults who had never fallen were assessed on 73 variables, comprising medical, social and physical outcomes, at t0. Each fall was recorded at a prospective 1-year follow-up. A decision tree was built on a randomly selected training subset of the cohort (80% of the full-set) and validated on an independent test set. 82 participants experienced a first fall during the follow-up. The machine learning process independently extracted 13 powerful parameters and built a model showing 89% of accuracy for the overall classification with 83%-82% of true positive fallers and 96%-61% of true negative non-fallers (training set vs. independent test set). This study provides a pilot tool that could easily help the gerontologists refine the evaluation of the risk of the first fall onset and prioritize the effective prevention strategies. The study also offers a transparent framework for future, related investigation that would validate the clinical relevance of the established model by independently testing its accuracy on larger cohort. Copyright © 2016 Elsevier Inc. All rights reserved.

The “Dry-Run” Analysis: A Method for Evaluating Risk Scores for Confounding Control

PubMed Central

Wyss, Richard; Hansen, Ben B.; Ellis, Alan R.; Gagne, Joshua J.; Desai, Rishi J.; Glynn, Robert J.; Stürmer, Til

2017-01-01

Abstract A propensity score (PS) model's ability to control confounding can be assessed by evaluating covariate balance across exposure groups after PS adjustment. The optimal strategy for evaluating a disease risk score (DRS) model's ability to control confounding is less clear. DRS models cannot be evaluated through balance checks within the full population, and they are usually assessed through prediction diagnostics and goodness-of-fit tests. A proposed alternative is the “dry-run” analysis, which divides the unexposed population into “pseudo-exposed” and “pseudo-unexposed” groups so that differences on observed covariates resemble differences between the actual exposed and unexposed populations. With no exposure effect separating the pseudo-exposed and pseudo-unexposed groups, a DRS model is evaluated by its ability to retrieve an unconfounded null estimate after adjustment in this pseudo-population. We used simulations and an empirical example to compare traditional DRS performance metrics with the dry-run validation. In simulations, the dry run often improved assessment of confounding control, compared with the C statistic and goodness-of-fit tests. In the empirical example, PS and DRS matching gave similar results and showed good performance in terms of covariate balance (PS matching) and controlling confounding in the dry-run analysis (DRS matching). The dry-run analysis may prove useful in evaluating confounding control through DRS models. PMID:28338910

Using a Systematic Conceptual Model for a Process Evaluation of a Middle School Obesity Risk-Reduction Nutrition Curriculum Intervention: Choice, Control & Change

PubMed Central

Lee, Heewon; Contento, Isobel R.; Koch, Pamela

2012-01-01

Objective To use and review a conceptual model of process evaluation and to examine the implementation of a nutrition education curriculum, Choice, Control & Change, designed to promote dietary and physical activity behaviors that reduce obesity risk. Design A process evaluation study based on a systematic conceptual model. Setting Five middle schools in New York City. Participants 562 students in 20 classes and their science teachers (n=8). Main Outcome Measures Based on the model, teacher professional development, teacher implementation, and student reception were evaluated. Also measured were teacher characteristics, teachers’ curriculum evaluation, and satisfaction with teaching the curriculum. Analysis Descriptive statistics and Spearman’s Rho Correlation for quantitative analysis and content analysis for qualitative data were used. Results Mean score of the teacher professional development evaluation was 4.75 on a 5-point scale. Average teacher implementation rate was 73%, and student reception rate was 69%. Ongoing teacher support was highly valued by teachers. Teachers’ satisfaction with teaching the curriculum was highly correlated with students’ satisfaction (p <.05). Teachers’ perception of amount of student work was negatively correlated with implementation and with student satisfaction (p<.05). Conclusions and implications Use of a systematic conceptual model and comprehensive process measures improves understanding of the implementation process and helps educators to better implement interventions as designed. PMID:23321021

Using a systematic conceptual model for a process evaluation of a middle school obesity risk-reduction nutrition curriculum intervention: choice, control & change.

PubMed

Lee, Heewon; Contento, Isobel R; Koch, Pamela

2013-03-01

To use and review a conceptual model of process evaluation and to examine the implementation of a nutrition education curriculum, Choice, Control & Change, designed to promote dietary and physical activity behaviors that reduce obesity risk. A process evaluation study based on a systematic conceptual model. Five middle schools in New York City. Five hundred sixty-two students in 20 classes and their science teachers (n = 8). Based on the model, teacher professional development, teacher implementation, and student reception were evaluated. Also measured were teacher characteristics, teachers' curriculum evaluation, and satisfaction with teaching the curriculum. Descriptive statistics and Spearman ρ correlation for quantitative analysis and content analysis for qualitative data were used. Mean score of the teacher professional development evaluation was 4.75 on a 5-point scale. Average teacher implementation rate was 73%, and the student reception rate was 69%. Ongoing teacher support was highly valued by teachers. Teacher satisfaction with teaching the curriculum was highly correlated with student satisfaction (P < .05). Teacher perception of amount of student work was negatively correlated with implementation and with student satisfaction (P < .05). Use of a systematic conceptual model and comprehensive process measures improves understanding of the implementation process and helps educators to better implement interventions as designed. Copyright © 2013 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Subject-enabled analytics model on measurement statistics in health risk expert system for public health informatics.

PubMed

Chung, Chi-Jung; Kuo, Yu-Chen; Hsieh, Yun-Yu; Li, Tsai-Chung; Lin, Cheng-Chieh; Liang, Wen-Miin; Liao, Li-Na; Li, Chia-Ing; Lin, Hsueh-Chun

2017-11-01

This study applied open source technology to establish a subject-enabled analytics model that can enhance measurement statistics of case studies with the public health data in cloud computing. The infrastructure of the proposed model comprises three domains: 1) the health measurement data warehouse (HMDW) for the case study repository, 2) the self-developed modules of online health risk information statistics (HRIStat) for cloud computing, and 3) the prototype of a Web-based process automation system in statistics (PASIS) for the health risk assessment of case studies with subject-enabled evaluation. The system design employed freeware including Java applications, MySQL, and R packages to drive a health risk expert system (HRES). In the design, the HRIStat modules enforce the typical analytics methods for biomedical statistics, and the PASIS interfaces enable process automation of the HRES for cloud computing. The Web-based model supports both modes, step-by-step analysis and auto-computing process, respectively for preliminary evaluation and real time computation. The proposed model was evaluated by computing prior researches in relation to the epidemiological measurement of diseases that were caused by either heavy metal exposures in the environment or clinical complications in hospital. The simulation validity was approved by the commercial statistics software. The model was installed in a stand-alone computer and in a cloud-server workstation to verify computing performance for a data amount of more than 230K sets. Both setups reached efficiency of about 10 5 sets per second. The Web-based PASIS interface can be used for cloud computing, and the HRIStat module can be flexibly expanded with advanced subjects for measurement statistics. The analytics procedure of the HRES prototype is capable of providing assessment criteria prior to estimating the potential risk to public health. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2013-04-18

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

Geographic exposure risk of variant Creutzfeldt-Jakob disease in US blood donors: a risk-ranking model to evaluate alternative donor-deferral policies.

PubMed

Yang, Hong; Huang, Yin; Gregori, Luisa; Asher, David M; Bui, Travis; Forshee, Richard A; Anderson, Steven A

2017-04-01

Variant Creutzfeldt-Jakob disease (vCJD) has been transmitted by blood transfusion (TTvCJD). The US Food and Drug Administration (FDA) recommends deferring blood donors who resided in or traveled to 30 European countries where they may have been exposed to bovine spongiform encephalopathy (BSE) through beef consumption. Those recommendations warrant re-evaluation, because new cases of BSE and vCJD have markedly abated. The FDA developed a risk-ranking model to calculate the geographic vCJD risk using country-specific case rates and person-years of exposure of US blood donors. We used the reported country vCJD case rates, when available, or imputed vCJD case rates from reported BSE and UK beef exports during the risk period. We estimated the risk reduction and donor loss should the deferral be restricted to a few high-risk countries. We also estimated additional risk reduction by leukocyte reduction (LR) of red blood cells (RBCs). The United Kingdom, Ireland, and France had the greatest vCJD risk, contributing approximately 95% of the total risk. The model estimated that deferring US donors who spent extended periods of time in these three countries, combined with currently voluntary LR (95% of RBC units), would reduce the vCJD risk by 89.3%, a reduction similar to that achieved under the current policy (89.8%). Limiting deferrals to exposure in these three countries would potentially allow donations from an additional 100,000 donors who are currently deferred. Our analysis suggests that a deferral option focusing on the three highest risk countries would achieve a level of blood safety similar to that achieved by the current policy. © 2016 AABB.

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

PubMed

Sun, Xiangqing; Elston, Robert C; Barnholtz-Sloan, Jill S; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Tian, Ye D; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford D; Chandar, Apoorva; Warfe, James M; Brock, Wendy; Chak, Amitabh

2016-05-01

Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR. ©2016 American Association for Cancer Research.

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

NASA Astrophysics Data System (ADS)

Diaferio, Mariella; Foti, Dora

2017-09-01

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

Anxiety after completion of treatment for early-stage breast cancer: a systematic review to identify candidate predictors and evaluate multivariable model development.

PubMed

Harris, Jenny; Cornelius, Victoria; Ream, Emma; Cheevers, Katy; Armes, Jo

2017-07-01

The purpose of this review was to identify potential candidate predictors of anxiety in women with early-stage breast cancer (BC) after adjuvant treatments and evaluate methodological development of existing multivariable models to inform the future development of a predictive risk stratification model (PRSM). Databases (MEDLINE, Web of Science, CINAHL, CENTRAL and PsycINFO) were searched from inception to November 2015. Eligible studies were prospective, recruited women with stage 0-3 BC, used a validated anxiety outcome ≥3 months post-treatment completion and used multivariable prediction models. Internationally accepted quality standards were used to assess predictive risk of bias and strength of evidence. Seven studies were identified: five were observational cohorts and two secondary analyses of RCTs. Variability of measurement and selective reporting precluded meta-analysis. Twenty-one candidate predictors were identified in total. Younger age and previous mental health problems were identified as risk factors in ≥3 studies. Clinical variables (e.g. treatment, tumour grade) were not identified as predictors in any studies. No studies adhered to all quality standards. Pre-existing vulnerability to mental health problems and younger age increased the risk of anxiety after completion of treatment for BC survivors, but there was no evidence that chemotherapy was a predictor. Multiple predictors were identified but many lacked reproducibility or were not measured across studies, and inadequate reporting did not allow full evaluation of the multivariable models. The use of quality standards in the development of PRSM within supportive cancer care would improve model quality and performance, thereby allowing professionals to better target support for patients.

The Relative Impact of Climate Change on the Extinction Risk of Tree Species in the Montane Tropical Andes.

PubMed

Tejedor Garavito, Natalia; Newton, Adrian C; Golicher, Duncan; Oldfield, Sara

2015-01-01

There are widespread concerns that anthropogenic climate change will become a major cause of global biodiversity loss. However, the potential impact of climate change on the extinction risk of species remains poorly understood, particularly in comparison to other current threats. The objective of this research was to examine the relative impact of climate change on extinction risk of upper montane tree species in the tropical Andes, an area of high biodiversity value that is particularly vulnerable to climate change impacts. The extinction risk of 129 tree species endemic to the region was evaluated according to the IUCN Red List criteria, both with and without the potential impacts of climate change. Evaluations were supported by development of species distribution models, using three methods (generalized additive models, recursive partitioning, and support vector machines), all of which produced similarly high AUC values when averaged across all species evaluated (0.82, 0.86, and 0.88, respectively). Inclusion of climate change increased the risk of extinction of 18-20% of the tree species evaluated, depending on the climate scenario. The relative impact of climate change was further illustrated by calculating the Red List Index, an indicator that shows changes in the overall extinction risk of sets of species over time. A 15% decline in the Red List Index was obtained when climate change was included in this evaluation. While these results suggest that climate change represents a significant threat to tree species in the tropical Andes, they contradict previous suggestions that climate change will become the most important cause of biodiversity loss in coming decades. Conservation strategies should therefore focus on addressing the multiple threatening processes currently affecting biodiversity, rather than focusing primarily on potential climate change impacts.

The Relative Impact of Climate Change on the Extinction Risk of Tree Species in the Montane Tropical Andes

PubMed Central

Tejedor Garavito, Natalia; Newton, Adrian C.; Golicher, Duncan; Oldfield, Sara

2015-01-01

There are widespread concerns that anthropogenic climate change will become a major cause of global biodiversity loss. However, the potential impact of climate change on the extinction risk of species remains poorly understood, particularly in comparison to other current threats. The objective of this research was to examine the relative impact of climate change on extinction risk of upper montane tree species in the tropical Andes, an area of high biodiversity value that is particularly vulnerable to climate change impacts. The extinction risk of 129 tree species endemic to the region was evaluated according to the IUCN Red List criteria, both with and without the potential impacts of climate change. Evaluations were supported by development of species distribution models, using three methods (generalized additive models, recursive partitioning, and support vector machines), all of which produced similarly high AUC values when averaged across all species evaluated (0.82, 0.86, and 0.88, respectively). Inclusion of climate change increased the risk of extinction of 18–20% of the tree species evaluated, depending on the climate scenario. The relative impact of climate change was further illustrated by calculating the Red List Index, an indicator that shows changes in the overall extinction risk of sets of species over time. A 15% decline in the Red List Index was obtained when climate change was included in this evaluation. While these results suggest that climate change represents a significant threat to tree species in the tropical Andes, they contradict previous suggestions that climate change will become the most important cause of biodiversity loss in coming decades. Conservation strategies should therefore focus on addressing the multiple threatening processes currently affecting biodiversity, rather than focusing primarily on potential climate change impacts. PMID:26177097

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

Social ecological determinants of youth violence among ethnically diverse Asian and Pacific Islander students.

PubMed

Goebert, Deborah; Chang, Janice Y; Chung-Do, Jane; Else, 'Iwalani R N; Hamagami, Fumiaki; Helm, Susana; Kinkade, Katie; Sugimoto-Matsuda, Jeanelle J

2012-01-01

This study assesses the relative fit of risk/protective and social ecological models of youth violence among predominantly Asian and Pacific Islander students. Data from a 2007 survey of two multi-ethnic high schools in Hawai'i were used. The survey assessed interpersonal youth violence, suicidality and risk and protective factors. Two models of youth violence (risk/protective and social ecological) were tested using structural equation modeling. We found good fits for the risk/protective model (χ(2) = 369.42, df = 77, P < .0001; CFI = .580; RMSEA = .066) and the ecological model (χ(2) = 1763.65, df = 292, P < .0001; CFI = .636; RMSEA = .076). The risk/protective model showed the importance of coping skills. However, the ecological model allowed examination of the interconnectivity among factors. Peer exposure to violence had no direct influence on individuals and peer influence was fully mediated by school climate. Furthermore, family factors directly contributed to peer exposure, community, and individual risk/protection. These findings have significant implications for intervention and prevention efforts and for the promotion of positive, competent, and healthy youth development. While few family and school-based programs have been developed and evaluated for adolescents, they have the greatest potential for success.

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.

PubMed

Sakoda, Lori C; Henderson, Louise M; Caverly, Tanner J; Wernli, Karen J; Katki, Hormuzd A

2017-12-01

Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

Risk to Water Security on Small Islands

NASA Astrophysics Data System (ADS)

Holding, S. T.; Allen, D. M.

2013-12-01

The majority of fresh water available on small islands is shallow groundwater that forms a freshwater lens. Freshwater lenses are generally limited in extent and as such are vulnerable to many stressors that impact water security. These include stressors related to climate change, such as sea level rise, as well as those related to human impacts, such as contamination. Traditionally, water security assessments have focussed on indicators that provide a snapshot of the current condition. However, recent work suggests that in order to effectively manage the water system, it is also important to consider uncertain future impacts to the system by evaluating how different stressors might impact water security. In this study, a framework for assessing risk to water security was developed and tested on Andros Island in The Bahamas. The assessment comprises two main components that characterise the water system: numerical modelling studies and a hazard survey. A baseline numerical model of the freshwater lens throughout Andros Island was developed to simulate the morphology of the freshwater lens and estimate the freshwater resources currently available. The model was prepared using SEAWAT, a density-dependent flow and solute transport code. Various stressors were simulated in the model to evaluate the response of the freshwater lens to predicted future shifts in climate patterns, sea level rise, and changes in water use. A hazard survey was also conducted on the island to collect information related to the storage of contaminants, sanitation infrastructure, waste disposal practices and groundwater abstraction rates. The results of the survey form a geo-spatial database of the location and associated hazards to the freshwater lens. The resulting risk framework provides a ranking of overall risk to water security based on information from the numerical modelling and hazard survey. The risk framework is implemented in a Geographic Information System (GIS) and provides a map of the risk to water security throughout Andros Island. It evaluates risk to water security for current and future scenarios and will enable water resource managers to effectively adapt to future impacts on water security.

A Risk Prediction Model for Sporadic CRC Based on Routine Lab Results.

PubMed

Boursi, Ben; Mamtani, Ronac; Hwang, Wei-Ting; Haynes, Kevin; Yang, Yu-Xiao

2016-07-01

Current risk scores for colorectal cancer (CRC) are based on demographic and behavioral factors and have limited predictive values. To develop a novel risk prediction model for sporadic CRC using clinical and laboratory data in electronic medical records. We conducted a nested case-control study in a UK primary care database. Cases included those with a diagnostic code of CRC, aged 50-85. Each case was matched with four controls using incidence density sampling. CRC predictors were examined using univariate conditional logistic regression. Variables with p value <0.25 in the univariate analysis were further evaluated in multivariate models using backward elimination. Discrimination was assessed using receiver operating curve. Calibration was evaluated using the McFadden's R2. Net reclassification index (NRI) associated with incorporation of laboratory results was calculated. Results were internally validated. A model similar to existing CRC prediction models which included age, sex, height, obesity, ever smoking, alcohol dependence, and previous screening colonoscopy had an AUC of 0.58 (0.57-0.59) with poor goodness of fit. A laboratory-based model including hematocrit, MCV, lymphocytes, and neutrophil-lymphocyte ratio (NLR) had an AUC of 0.76 (0.76-0.77) and a McFadden's R2 of 0.21 with a NRI of 47.6 %. A combined model including sex, hemoglobin, MCV, white blood cells, platelets, NLR, and oral hypoglycemic use had an AUC of 0.80 (0.79-0.81) with a McFadden's R2 of 0.27 and a NRI of 60.7 %. Similar results were shown in an internal validation set. A laboratory-based risk model had good predictive power for sporadic CRC risk.

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

PubMed

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

2015-11-20

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

[Clinical evaluation of the revised International Prognostic Score of Thrombosis for essential thrombocythemia (IPSET-thrombosis) in a cohort of 746 Chinese adult patients].

PubMed

Fu, R F; Li, H Y; Xue, F; Liu, X F; Liu, W; Huang, Y T; Chen, Y F; Zhang, L Y; Zhang, L; Yang, R C

2017-02-14

Objective: To evaluate the role of the revised International Prognostic Score of Thrombosis (IPSET-thrombosis) in predicting the occurrence of thrombotic events in Chinese patients with essential thrombocythemia (ET) and to develop a thrombosis predicting model more applicable to Chinese ET patients. Methods: Medical records of 746 adult patients with an initial diagnosis of ET were retrospectively analyzed. Results: The median age at diagnosis was 52 (18-87) years, with 305 males and 441 females. According to the revised IPSET-thrombosis model, the number of very low-, low-, intermediate-, and high-risk patients were 271 (36.3%) , 223 (29.9%) , 63 (8.4%) and 189 (25.3%) , respectively. The four groups exhibited significantly different thrombosis-free survival ( χ (2)=72.301, P <0.001) . Thirty-six patients were reclassified as intermediate-risk according to the revised IPSET-thrombosis instead of low-risk as per the original IPSET-thrombosis. Nineteen intermediate-risk patients as per the original IPSET-thrombosis were upgraded to high-risk according to the revised IPSET-thrombosis. Fifty-one high-risk patients as per the original IPSET-thrombosis were reclassified as low-risk in the revised IPSET-thrombosis. It suggests that the revised IPSET-thrombosis potentially avoids over- or under-treatment. In low-risk patients as per the revised IPSET-thrombosis, the rate of thrombosis in patients with cardiovascular risk factors (CVF) was higher than that in those without (16.3% vs 5.2%, χ (2)=5.264, P =0.022) , and comparable with intermediate-risk patients as per the revised IPSET-thrombosis (16.3% vs 14.3%, χ (2)=0.089, P =0.765) . As a result, a new revised IPSET-thrombosis model more applicable to Chinese ET patients was developed in which patients with CVF in the low-risk group as per the revised IPSET-thrombosis were reclassified as intermediate-risk group. Conclusion: For predicting the occurrence of thrombotic events, the revised IPSET-thrombosis model was better than the original IPSET-thrombosis model. The revised IPSET-thrombosis was optimized and a new revised IPSET-thrombosis model more applicable to Chinese ET patients was developed, and the new evidence for risk stratification and treatment of ET in Chinese was provided.

Cardiovascular risk assessment in elderly adults using SCORE OP model in a Latin American population: The experience from Ecuador.

PubMed

Sisa, Ivan

2018-02-09

Cardiovascular disease (CVD) mortality is predicted to increase in Latin America countries due to their rapidly aging population. However, there is very little information about CVD risk assessment as a primary preventive measure in this high-risk population. We predicted the national risk of developing CVD in Ecuadorian elderly population using the Systematic COronary Risk Evaluation in Older Persons (SCORE OP) High and Low models by risk categories/CVD risk region in 2009. Data on national cardiovascular risk factors were obtained from the Encuesta sobre Salud, Bienestar y Envejecimiento. We computed the predicted 5-year risk of CVD risk and compared the extent of agreement and reclassification in stratifying high-risk individuals between SCORE OP High and Low models. Analyses were done by risk categories, CVD risk region, and sex. In 2009, based on SCORE OP Low model almost 42% of elderly adults living in Ecuador were at high risk of suffering CVD over a 5-year period. The extent of agreement between SCORE OP High and Low risk prediction models was moderate (Cohen's kappa test of 0.5), 34% of individuals approximately were reclassified into different risk categories and a third of the population would benefit from a pharmacologic intervention to reduce the CVD risk. Forty-two percent of elderly Ecuadorians were at high risk of suffering CVD over a 5-year period, indicating an urgent need to tailor primary preventive measures for this vulnerable and high-risk population. Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

Assessment of BTEX-induced health risk under multiple uncertainties at a petroleum-contaminated site: An integrated fuzzy stochastic approach

NASA Astrophysics Data System (ADS)

Zhang, Xiaodong; Huang, Guo H.

2011-12-01

Groundwater pollution has gathered more and more attention in the past decades. Conducting an assessment of groundwater contamination risk is desired to provide sound bases for supporting risk-based management decisions. Therefore, the objective of this study is to develop an integrated fuzzy stochastic approach to evaluate risks of BTEX-contaminated groundwater under multiple uncertainties. It consists of an integrated interval fuzzy subsurface modeling system (IIFMS) and an integrated fuzzy second-order stochastic risk assessment (IFSOSRA) model. The IIFMS is developed based on factorial design, interval analysis, and fuzzy sets approach to predict contaminant concentrations under hybrid uncertainties. Two input parameters (longitudinal dispersivity and porosity) are considered to be uncertain with known fuzzy membership functions, and intrinsic permeability is considered to be an interval number with unknown distribution information. A factorial design is conducted to evaluate interactive effects of the three uncertain factors on the modeling outputs through the developed IIFMS. The IFSOSRA model can systematically quantify variability and uncertainty, as well as their hybrids, presented as fuzzy, stochastic and second-order stochastic parameters in health risk assessment. The developed approach haw been applied to the management of a real-world petroleum-contaminated site within a western Canada context. The results indicate that multiple uncertainties, under a combination of information with various data-quality levels, can be effectively addressed to provide supports in identifying proper remedial efforts. A unique contribution of this research is the development of an integrated fuzzy stochastic approach for handling various forms of uncertainties associated with simulation and risk assessment efforts.

[The model of perioperative risk assessment in elderly patients - interim analysis].

PubMed

Grabowska, Izabela; Ścisło, Lucyna; Pietruszka, Szymon; Walewska, Elzbieta; Paszko, Agata; Siarkiewicz, Benita; Richter, Piotr; Budzyński, Andrzej; Szczepanik, Antoni M

2017-04-21

Demographic changes in contemporary society require implementation of proper perioperative care of elderly patients due to an increased risk of perioperative complications in this group. Preoperative assessment of health status identifies risks and enables preventive interventions, improving outcomes of surgical treatment. The Comprehensive Geriatric Assessment contains numerous diagnostic tests and consultations, which is expensive and difficult to use in everyday practice. The development of a simplified model of perioperative assessment of elderly patients will help identifying the group of patients who require further diagnostic workup. The aim of the study is to evaluate the usefulness of the tests used in a proposed model of perioperative risk assessment in elderly patients. In a group of 178 patients older than 64 years admitted for surgical procedures, a battery of tests was performed. The proposed model of perioperative risk assessment included: Charlson Comorbidity Index, ADL (activities of daily living), TUG test (timed "up and go" test), MNA (mini nutritional assessment), AMTS (abbreviated mental test score), spirometry measurement of respiratory muscle strength (Pimax, Pemax). Distribution of abnormal results of each test has been analysed. The Charlson Index over 6 points was recorded in 10.1% of patients (15.1% in cancer patients). Abnormal result of the TUG test was observed in 32.1%. The risk of malnutrition in MNA test has been identified in 29.7% (39.2% in cancer patients). Abnormal test results at the level of 10-30% indicate potential diagnostic value of Charlson Comorbidity Index, TUG test and MNA in the evaluation of perioperative risk in elderly patients.

A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model

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

Pasqualini, Donatella

This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimatedmore » stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.« less

Introduction of a new laboratory test: an econometric approach with the use of neural network analysis.

PubMed

Jabor, A; Vlk, T; Boril, P

1996-04-15

We designed a simulation model for the assessment of the financial risks involved when a new diagnostic test is introduced in the laboratory. The model is based on a neural network consisting of ten neurons and assumes that input entities can have assigned appropriate uncertainty. Simulations are done on a 1-day interval basis. Risk analysis completes the model and the financial effects are evaluated for a selected time period. The basic output of the simulation consists of total expenses and income during the simulation time, net present value of the project at the end of simulation, total number of control samples during simulation, total number of patients evaluated and total number of used kits.

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

PubMed

Pouillot, Régis; Chen, Yuhuan; Hoelzer, Karin

2015-02-01

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

Confirmation of Models for Interpretation and Use of the Social and Academic Behavior Risk Screener (SABRS)

ERIC Educational Resources Information Center

Kilgus, Stephen P.; Sims, Wesley A.; von der Embse, Nathaniel P.; Riley-Tillman, T. Chris

2015-01-01

The purpose of this investigation was to evaluate the models for interpretation and use that serve as the foundation of an interpretation/use argument for the Social and Academic Behavior Risk Screener (SABRS). The SABRS was completed by 34 teachers with regard to 488 students in a Midwestern high school during the winter portion of the academic…

Advantages of new cardiovascular risk-assessment strategies in high-risk patients with hypertension.

PubMed

Ruilope, Luis M; Segura, Julian

2005-10-01

Accurate assessment of cardiovascular disease (CVD) risk in patients with hypertension is important when planning appropriate treatment of modifiable risk factors. The causes of CVD are multifactorial, and hypertension seldom exists as an isolated risk factor. Classic models of risk assessment are more accurate than a simple counting of risk factors, but they are not generalizable to all populations. In addition, the risk associated with hypertension is graded, continuous, and independent of other risk factors, and this is not reflected in classic models of risk assessment. This article is intended to review both classic and newer models of CVD risk assessment. MEDLINE was searched for articles published between 1990 and 2005 that contained the terms cardiovascular disease, hypertension, or risk assessment. Articles describing major clinical trials, new data about cardiovascular risk, or global risk stratification were selected for review. Some patients at high long-term risk for CVD events (eg, patients aged <50 years with multiple risk factors) may go untreated because they do not meet the absolute risk-intervention threshold of 20% risk over 10 years with the classic model. Recognition of the limitations of classic risk-assessment models led to new guidelines, particularly those of the European Society of Hypertension-European Society of Cardiology. These guidelines view hypertension as one of many risk and disease factors that require treatment to decrease risk. These newer guidelines include a more comprehensive range of risk factors and more finely graded blood pressure ranges to stratify patients by degree of risk. Whether they accurately predict CVD risk in most populations is not known. Evidence from the Valsartan Antihypertensive Long-term Use Evaluation (VALUE) study, which stratified patients by several risk and disease factors, highlights the predictive value of some newer CVD risk assessments. Modern risk assessments, which include blood pressure along with a wide array of modifiable risk factors, may be more accurate than classic models for CVD risk prediction.

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

PubMed Central

2018-01-01

ABSTRACT Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public. PMID:29887766

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

DTIC Science & Technology

2012-01-01

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

Providing Home Visiting to High-Risk Pregnant and Postpartum Families: The Development and Evaluation of the MOMobile® Program

ERIC Educational Resources Information Center

Hadley, Barbara; Rudolph, Kara E.; Mogul, Marjie; Perry, Deborah F.

2014-01-01

Maternal, Infant, and Early Childhood Home Visiting legislation permits states to fund "promising practices"--with the understanding that these models will have a rigorous evaluation component. This article describes an innovative, low cost paraprofessional home visiting model developed in Pennsylvania by the Maternity Care Coalition. In…

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 School-Based Evaluation Model for Accelerating the Education of Students At-Risk.

ERIC Educational Resources Information Center

Fetterman, David M.; Haertel, Edward H.

This paper presents ideas for the development and utilization of a comprehensive evaluation plan for an accelerated school. It contains information about the purposes of a comprehensive evaluation, the evaluation design, and the kinds of data that might be gathered and used. The first section, "An Approach to Evaluation: Multiple Purposes and…

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

An Integrated Risk Management Model for Source Water Protection Areas

PubMed Central

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

2012-01-01

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

The Risk-Informed Materials Management (RIMM) Tool System for Determining Safe-Levels of Contaminated Materials Managed on the Land

EPA Science Inventory

EPA’s Risk-Informed Materials Management (RIMM) tool system is a modeling approach that helps risk assessors evaluate the safety of managing raw, reused, or waste material streams via a variety of common scenarios (e.g., application to farms, use as a component in road cons...

Utility of different cardiovascular disease prediction models in rheumatoid arthritis.

PubMed

Purcarea, A; Sovaila, S; Udrea, G; Rezus, E; Gheorghe, A; Tiu, C; Stoica, V

2014-01-01

Rheumatoid arthritis comes with a 30% higher probability for cardiovascular disease than the general population. Current guidelines advocate for early and aggressive primary prevention and treatment of risk factors in high-risk populations but this excess risk is under-addressed in RA in real life. This is mainly due to difficulties met in the correct risk evaluation. This study aims to underline the differences in results of the main cardiovascular risk screening models in the real life rheumatoid arthritis population. In a cross-sectional study, patients addressed to a tertiary care center in Romania for an biannual follow-up of rheumatoid arthritis and the ones who were considered free of any cardiovascular disease were assessed for subclinical atherosclerosis. Clinical, biological and carotidal ultrasound evaluations were performed. A number of cardiovascular disease prediction scores were performed and differences between tests were noted in regard to subclinical atherosclerosis as defined by the existence of carotid intima media thickness over 0,9 mm or carotid plaque. In a population of 29 Romanian rheumatoid arthritis patients free of cardiovascular disease, the performance of Framingham Risk Score, HeartSCORE, ARIC cardiovascular disease prediction score, Reynolds Risk Score, PROCAM risk score and Qrisk2 score were compared. All the scores under-diagnosed subclinical atherosclerosis. With an AUROC of 0,792, the SCORE model was the only one that could partially stratify patients in low, intermediate and high-risk categories. The use of the EULAR recommended modifier did not help to reclassify patients. The only score that showed a statistically significant prediction capacity for subclinical atherosclerosis in a Romanian rheumatoid arthritis population was SCORE. The additional calibration or the use of imaging techniques in CVD risk prediction for the intermediate risk category might be warranted.

Utility of different cardiovascular disease prediction models in rheumatoid arthritis

PubMed Central

Purcarea, A; Sovaila, S; Udrea, G; Rezus, E; Gheorghe, A; Tiu, C; Stoica, V

2014-01-01

Background. Rheumatoid arthritis comes with a 30% higher probability for cardiovascular disease than the general population. Current guidelines advocate for early and aggressive primary prevention and treatment of risk factors in high-risk populations but this excess risk is under-addressed in RA in real life. This is mainly due to difficulties met in the correct risk evaluation. This study aims to underline the differences in results of the main cardiovascular risk screening models in the real life rheumatoid arthritis population. Methods. In a cross-sectional study, patients addressed to a tertiary care center in Romania for an biannual follow-up of rheumatoid arthritis and the ones who were considered free of any cardiovascular disease were assessed for subclinical atherosclerosis. Clinical, biological and carotidal ultrasound evaluations were performed. A number of cardiovascular disease prediction scores were performed and differences between tests were noted in regard to subclinical atherosclerosis as defined by the existence of carotid intima media thickness over 0,9 mm or carotid plaque. Results. In a population of 29 Romanian rheumatoid arthritis patients free of cardiovascular disease, the performance of Framingham Risk Score, HeartSCORE, ARIC cardiovascular disease prediction score, Reynolds Risk Score, PROCAM risk score and Qrisk2 score were compared. All the scores under-diagnosed subclinical atherosclerosis. With an AUROC of 0,792, the SCORE model was the only one that could partially stratify patients in low, intermediate and high-risk categories. The use of the EULAR recommended modifier did not help to reclassify patients. Conclusion. The only score that showed a statistically significant prediction capacity for subclinical atherosclerosis in a Romanian rheumatoid arthritis population was SCORE. The additional calibration or the use of imaging techniques in CVD risk prediction for the intermediate risk category might be warranted. PMID:25713628

Hydrological Modelling using HEC-HMS for Flood Risk Assessment of Segamat Town, Malaysia

NASA Astrophysics Data System (ADS)

Romali, N. S.; Yusop, Z.; Ismail, A. Z.

2018-03-01

This paper presents an assessment of the applicability of using Hydrologic Modelling System developed by the Hydrologic Engineering Center (HEC-HMS) for hydrological modelling of Segamat River. The objective of the model application is to assist in the assessment of flood risk by providing the peak flows of 2011 Segamat flood for the generation of flood mapping of Segamat town. The capability of the model was evaluated by comparing the historical observed data with the simulation results of the selected flood events. The model calibration and validation efficiency was verified using Nash-Sutcliffe model efficiency coefficient. The results demonstrate the interest to implement the hydrological model for assessing flood risk where the simulated peak flow result is in agreement with historical observed data. The model efficiency of the calibrated and validated exercises is 0.90 and 0.76 respectively, which is acceptable.

Problems With Risk Reclassification Methods for Evaluating Prediction Models

PubMed Central

Pepe, Margaret S.

2011-01-01

For comparing the performance of a baseline risk prediction model with one that includes an additional predictor, a risk reclassification analysis strategy has been proposed. The first step is to cross-classify risks calculated according to the 2 models for all study subjects. Summary measures including the percentage of reclassification and the percentage of correct reclassification are calculated, along with 2 reclassification calibration statistics. The author shows that interpretations of the proposed summary measures and P values are problematic. The author's recommendation is to display the reclassification table, because it shows interesting information, but to use alternative methods for summarizing and comparing model performance. The Net Reclassification Index has been suggested as one alternative method. The author argues for reporting components of the Net Reclassification Index because they are more clinically relevant than is the single numerical summary measure. PMID:21555714

Forest-fire models

Treesearch

Haiganoush Preisler; Alan Ager

2013-01-01

For applied mathematicians forest fire models refer mainly to a non-linear dynamic system often used to simulate spread of fire. For forest managers forest fire models may pertain to any of the three phases of fire management: prefire planning (fire risk models), fire suppression (fire behavior models), and postfire evaluation (fire effects and economic models). In...

A framework for global river flood risk assessment

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Bouwman, A.; Ward, P. J.; Jongman, B.

2012-04-01

There is an increasing need for strategic global assessments of flood risks. Such assessments may be required by: (a) International Financing Institutes and Disaster Management Agencies to evaluate where, when, and which investments in flood risk mitigation are most required; (b) (re-)insurers, who need to determine their required coverage capital; and (c) large companies to account for risks of regional investments. In this contribution, we propose a framework for global river flood risk assessment. The framework combines coarse scale resolution hazard probability distributions, derived from global hydrological model runs (typical scale about 0.5 degree resolution) with high resolution estimates of exposure indicators. The high resolution is required because floods typically occur at a much smaller scale than the typical resolution of global hydrological models, and exposure indicators such as population, land use and economic value generally are strongly variable in space and time. The framework therefore estimates hazard at a high resolution ( 1 km2) by using a) global forcing data sets of the current (or in scenario mode, future) climate; b) a global hydrological model; c) a global flood routing model, and d) importantly, a flood spatial downscaling routine. This results in probability distributions of annual flood extremes as an indicator of flood hazard, at the appropriate resolution. A second component of the framework combines the hazard probability distribution with classical flood impact models (e.g. damage, affected GDP, affected population) to establish indicators for flood risk. The framework can be applied with a large number of datasets and models and sensitivities of such choices can be evaluated by the user. The framework is applied using the global hydrological model PCR-GLOBWB, combined with a global flood routing model. Downscaling of the hazard probability distributions to 1 km2 resolution is performed with a new downscaling algorithm, applied on a number of target regions. We demonstrate the use of impact models in these regions based on global GDP, population, and land use maps. In this application, we show sensitivities of the estimated risks with regard to the use of different climate input datasets, decisions made in the downscaling algorithm, and different approaches to establish distributed estimates of GDP and asset exposure to flooding.

Crystalline silica exposure and lung cancer mortality in diatomaceous earth industry workers: a quantitative risk assessment.

PubMed

Rice, F L; Park, R; Stayner, L; Smith, R; Gilbert, S; Checkoway, H

2001-01-01

To use various exposure-response models to estimate the risk of mortality from lung cancer due to occupational exposure to respirable crystalline silica dust. Data from a cohort mortality study of 2342 white male California diatomaceous earth mining and processing workers exposed to crystalline silica dust (mainly cristobalite) were reanalyzed with Poisson regression and Cox's proportional hazards models. Internal and external adjustments were used to control for potential confounding from the effects of time since first observation, calendar time, age, and Hispanic ethnicity. Cubic smoothing spline models were used to assess the fit of the models. Exposures were lagged by 10 years. Evaluations of the fit of the models were performed by comparing their deviances. Lifetime risks of lung cancer were estimated up to age 85 with an actuarial approach that accounted for competing causes of death. Exposure to respirable crystalline silica dust was a significant predictor (p<0.05) in nearly all of the models evaluated and the linear relative rate model with a 10 year exposure lag seemed to give the best fit in the Poisson regression analysis. For those who died of lung cancer the linear relative rate model predicted rate ratios for mortality from lung cancer of about 1.6 for the mean cumulative exposure to respirable silica compared with no exposure. The excess lifetime risk (to age 85) of mortality from lung cancer for white men exposed for 45 years and with a 10 year lag period at the current Occupational Safety and Health Administration (OSHA) standard of about 0.05 mg/m(3) for respirable cristobalite dust is 19/1000 (95% confidence interval (95% CI) 5/1000 to 46/1000). There was a significant risk of mortality from lung cancer that increased with cumulative exposure to respirable crystalline silica dust. The predicted number of deaths from lung cancer suggests that current occupational health standards may not be adequately protecting workers from the risk of lung cancer.

A portfolio approach to evaluating natural hazard mitigation policies: An Application to lateral-spread ground failure in Coastal California

USGS Publications Warehouse

Bernknopf, R.L.; Dinitz, L.B.; Rabinovici, S.J.M.; Evans, A.M.

2001-01-01

In the past, efforts to prevent catastrophic losses from natural hazards have largely been undertaken by individual property owners based on site-specific evaluations of risks to particular buildings. Public efforts to assess community vulnerability and encourage mitigation have focused on either aggregating site-specific estimates or adopting standards based upon broad assumptions about regional risks. This paper develops an alternative, intermediate-scale approach to regional risk assessment and the evaluation of community mitigation policies. Properties are grouped into types with similar land uses and levels of hazard, and hypothetical community mitigation strategies for protecting these properties are modeled like investment portfolios. The portfolios consist of investments in mitigation against the risk to a community posed by a specific natural hazard, and are defined by a community's mitigation budget and the proportion of the budget invested in locations of each type. The usefulness of this approach is demonstrated through an integrated assessment of earthquake-induced lateral-spread ground failure risk in the Watsonville, California area. Data from the magnitude 6.9 Loma Prieta earthquake of 1989 are used to model lateral-spread ground failure susceptibility. Earth science and economic data are combined and analyzed in a Geographic Information System (GIS). The portfolio model is then used to evaluate the benefits of mitigating the risk in different locations. Two mitigation policies, one that prioritizes mitigation by land use type and the other by hazard zone, are compared with a status quo policy of doing no further mitigation beyond that which already exists. The portfolio representing the hazard zone rule yields a higher expected return than the land use portfolio does: However, the hazard zone portfolio experiences a higher standard deviation. Therefore, neither portfolio is clearly preferred. The two mitigation policies both reduce expected losses and increase overall expected community wealth compared to the status quo policy.

Model-based risk assessment and public health analysis to prevent Lyme disease

PubMed Central

Sabounchi, Nasim S.; Roome, Amanda; Spathis, Rita; Garruto, Ralph M.

2017-01-01

The number of Lyme disease (LD) cases in the northeastern United States has been dramatically increasing with over 300 000 new cases each year. This is due to numerous factors interacting over time including low public awareness of LD, risk behaviours and clothing choices, ecological and climatic factors, an increase in rodents within ecologically fragmented peri-urban built environments and an increase in tick density and infectivity in such environments. We have used a system dynamics (SD) approach to develop a simulation tool to evaluate the significance of risk factors in replicating historical trends of LD cases, and to investigate the influence of different interventions, such as increasing awareness, controlling clothing risk and reducing mouse populations, in reducing LD risk. The model accurately replicates historical trends of LD cases. Among several interventions tested using the simulation model, increasing public awareness most significantly reduces the number of LD cases. This model provides recommendations for LD prevention, including further educational programmes to raise awareness and control behavioural risk. This model has the potential to be used by the public health community to assess the risk of exposure to LD. PMID:29291075

Coastal flooding impact evaluation using an INtegrated DisRuption Assessment (INDRA) model for Varna region, Western Black Sea

NASA Astrophysics Data System (ADS)

Andreeva, Nataliya; Eftimova, Petya; Valchev, Nikolay; Prodanov, Bogdan

2017-04-01

The study presents evaluation and comparative analysis of storm induced flooding impacts on different coastal receptors at a scale of Varna region using INtegrated DisRuption Assessment (INDRA) model. The model was developed within the FP7 RISC-KIT project, as a part of Coastal Risk Assessment Framework (CRAF) consisting of two phases. CRAF Phase 1 is a screening process that evaluates coastal risk at a regional scale by means of coastal indices approach, which helps to identify potentially vulnerable coastal sectors: hot spots (HS). CRAF Phase 2 has the objective to assess and rank identified hotspots by detailed risk analysis done by jointly performing a hazard assessment and an impact evaluation on different categories (population, businesses, ecosystems, transport and utilities) using INDRA model at a regional level. Basically, the model assess the shock of events by estimating the impact on directly exposed to flooding hazard receptors of different vulnerability, as well as the potential ripple effects during an event in order to assess the "indirect" impacts, which occur outside the hazard area and/or continue after the event for all considered categories. The potential impacts are expressed in terms of uniform "Impact Indicators", which independently score the indirect impacts of these categories assessing disruption and recovery of the receptors. The ultimate hotspot ranking is obtained through the use of a Multi Criteria analysis (MCA) incorporated in the model, considering preferences of stakeholders. The case study area - Varna regional coast - is located on the western Black Sea, Bulgaria. The coastline, with a length of about 70 km, stretches from cape Ekrene to cape St. Atanas and includes Varna Bay. After application of CRAF Phase 1 three hotspots were selected for further analysis: Kabakum beach (HS1), Varna Central beach plus Port wall (HS2) and Artificial Island (HS3). For first two hotspots beaches and associated infrastructure are the assets that attract holiday-makers and tourists in summer season. For HS3 the exposed area is occupied by storage premises for industrial goods and oil/fuel tanks. Flooding hazard was assessed through coupled use of XBeach 1D and LISFLOOD 2D inundation models at the selected hotspots. The "response" approach was adopted as 75 extreme storm events were simulated to obtain storm maxima series of overtopping discharges, flood depth, depth-velocity and berm retreat. The selected return periods within the extreme value analysis were 20, 50 and 100 years. For impact evaluation by INDRA model the categories "Population" and "Business" were considered. Impacts on Population were addressed by 3 impact indicators: "Risk to Life", "Household Displacement Time" and "Household Financial Recovery", while for Business category only by "Business Financial Recovery". Hotspots ranking was done using MCA by weighting of the evaluated indicators: focused on Risk to Life (F1) and on Business Financial Recovery (F2). MCA scoring focused on Household displacement/recovery was not evaluated because modelling results revealed quite a low number of flooded household receptors. Results show that for both F1 and F2 and for all considered return periods HS2 has the highest scores, which makes it a final hotspot.

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

PubMed

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

2017-01-01

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

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...

Planning, Implementation, and Evaluation of AIDS Education Programs for Dentists.

ERIC Educational Resources Information Center

Gerbert, Barbara; And Others

1991-01-01

An office-based continuing education program on acquired immune deficiency syndrome (AIDS) for dentists is described, including needs assessment, model development, local piloting, national implementation with 119 dentists, and evaluation phases. Program evaluation indicated an improvement in risk perception, knowledge, and practice resulted, but…

How perfectionism and ineffectiveness influence growth of eating disorder risk in young adolescent girls.

PubMed

Wade, Tracey D; Wilksch, Simon M; Paxton, Susan J; Byrne, Susan M; Austin, S Bryn

2015-03-01

While perfectionism is widely considered to influence risk for eating disorders, results of longitudinal studies are mixed. The goal of the current study was to investigate a more complex model of how baseline perfectionism (both high personal standards and self-critical evaluative concerns) might influence change in risk status for eating disorders in young adolescent girls, through its influence on ineffectiveness. The study was conducted with 926 girls (mean age of 13 years), and involved three waves of data (baseline, 6- and 12-month follow-up). Latent growth curve modelling, incorporating the average rate at which risk changed over time, the intercept (initial status) of ineffectiveness, and baseline perfectionism, was used to explore longitudinal mediation. Personal standards was not supported as contributing to risk but results indicated that the higher mean scores on ineffectiveness over the three waves mediated the relationship between higher baseline self-critical evaluative concerns and both measures of eating disorder risk. The relationship between concern over mistakes and change in risk was small and negative. These results suggest the usefulness of interventions related to self-criticism and ineffectiveness for decreasing risk for developing an eating disorder in young adolescent girls. Copyright © 2015 Elsevier Ltd. All rights reserved.

Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation

NASA Technical Reports Server (NTRS)

2012-01-01

At the request of NASA, the National Research Council's (NRC's) Committee for Evaluation of Space Radiation Cancer Risk Model reviewed a number of changes that NASA proposes to make to its model for estimating the risk of radiation-induced cancer in astronauts. The NASA model in current use was last updated in 2005, and the proposed model would incorporate recent research directed at improving the quantification and understanding of the health risks posed by the space radiation environment. NASA's proposed model is defined by the 2011 NASA report Space Radiation Cancer Risk Projections and Uncertainties 2010 (Cucinotta et al., 2011). The committee's evaluation is based primarily on this source, which is referred to hereafter as the 2011 NASA report, with mention of specific sections or tables cited more formally as Cucinotta et al. (2011). The overall process for estimating cancer risks due to low linear energy transfer (LET) radiation exposure has been fully described in reports by a number of organizations. They include, more recently: (1) The "BEIR VII Phase 2" report from the NRC's Committee on Biological Effects of Ionizing Radiation (BEIR) (NRC, 2006); (2) Studies of Radiation and Cancer from the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR, 2006), (3) The 2007 Recommendations of the International Commission on Radiological Protection (ICRP), ICRP Publication 103 (ICRP, 2007); and (4) The Environmental Protection Agency s (EPA s) report EPA Radiogenic Cancer Risk Models and Projections for the U.S. Population (EPA, 2011). The approaches described in the reports from all of these expert groups are quite similar. NASA's proposed space radiation cancer risk assessment model calculates, as its main output, age- and gender-specific risk of exposure-induced death (REID) for use in the estimation of mission and astronaut-specific cancer risk. The model also calculates the associated uncertainties in REID. The general approach for estimating risk and uncertainty in the proposed model is broadly similar to that used for the current (2005) NASA model and is based on recommendations by the National Council on Radiation Protection and Measurements (NCRP, 2000, 2006). However, NASA's proposed model has significant changes with respect to the following: the integration of new findings and methods into its components by taking into account newer epidemiological data and analyses, new radiobiological data indicating that quality factors differ for leukemia and solid cancers, an improved method for specifying quality factors in terms of radiation track structure concepts as opposed to the previous approach based on linear energy transfer, the development of a new solar particle event (SPE) model, and the updates to galactic cosmic ray (GCR) and shielding transport models. The newer epidemiological information includes updates to the cancer incidence rates from the life span study (LSS) of the Japanese atomic bomb survivors (Preston et al., 2007), transferred to the U.S. population and converted to cancer mortality rates from U.S. population statistics. In addition, the proposed model provides an alternative analysis applicable to lifetime never-smokers (NSs). Details of the uncertainty analysis in the model have also been updated and revised. NASA's proposed model and associated uncertainties are complex in their formulation and as such require a very clear and precise set of descriptions. The committee found the 2011 NASA report challenging to review largely because of the lack of clarity in the model descriptions and derivation of the various parameters used. The committee requested some clarifications from NASA throughout its review and was able to resolve many, but not all, of the ambiguities in the written description.

Mathematical modelling of risk reduction in reinsurance

NASA Astrophysics Data System (ADS)

Balashov, R. B.; Kryanev, A. V.; Sliva, D. E.

2017-01-01

The paper presents a mathematical model of efficient portfolio formation in the reinsurance markets. The presented approach provides the optimal ratio between the expected value of return and the risk of yield values below a certain level. The uncertainty in the return values is conditioned by use of expert evaluations and preliminary calculations, which result in expected return values and the corresponding risk levels. The proposed method allows for implementation of computationally simple schemes and algorithms for numerical calculation of the numerical structure of the efficient portfolios of reinsurance contracts of a given insurance company.

Risk assessment of climate systems for national security.

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

Backus, George A.; Boslough, Mark Bruce Elrick; Brown, Theresa Jean

2012-10-01

Climate change, through drought, flooding, storms, heat waves, and melting Arctic ice, affects the production and flow of resource within and among geographical regions. The interactions among governments, populations, and sectors of the economy require integrated assessment based on risk, through uncertainty quantification (UQ). This project evaluated the capabilities with Sandia National Laboratories to perform such integrated analyses, as they relate to (inter)national security. The combining of the UQ results from climate models with hydrological and economic/infrastructure impact modeling appears to offer the best capability for national security risk assessments.

Risk Estimation Modeling and Feasibility Testing for a Mobile eHealth Intervention for Binge Drinking Among Young People: The D-ARIANNA (Digital-Alcohol RIsk Alertness Notifying Network for Adolescents and young adults) Project.

PubMed

Carrà, Giuseppe; Crocamo, Cristina; Schivalocchi, Alessandro; Bartoli, Francesco; Carretta, Daniele; Brambilla, Giulia; Clerici, Massimo

2015-01-01

Binge drinking is common among young people but often relevant risk factors are not recognized. eHealth apps, attractive for young people, may be useful to enhance awareness of this problem. We aimed at developing a current risk estimation model for binge drinking, incorporated into an eHealth app--D-ARIANNA (Digital-Alcohol RIsk Alertness Notifying Network for Adolescents and young adults)--for young people. A longitudinal approach with phase 1 (risk estimation), phase 2 (design), and phase 3 (feasibility) was followed. Risk/protective factors identified from the literature were used to develop a current risk estimation model for binge drinking. Relevant odds ratios were subsequently pooled through meta-analytic techniques with a random-effects model, deriving weighted estimates to be introduced in a final model. A set of questions, matching identified risk factors, were nested in a questionnaire and assessed for wording, content, and acceptability in focus groups involving 110 adolescents and young adults. Ten risk factors (5 modifiable) and 2 protective factors showed significant associations with binge drinking and were included in the model. Their weighted coefficients ranged between -0.71 (school proficiency) and 1.90 (cannabis use). The model, nested in an eHealth app questionnaire, provides in percent an overall current risk score, accompanied by appropriate images. Factors that mostly contribute are shown in summary messages. Minor changes have been realized after focus groups review. Most of the subjects (74%) regarded the eHealth app as helpful to assess binge drinking risk. We could produce an evidence-based eHealth app for young people, evaluating current risk for binge drinking. Its effectiveness will be tested in a large trial.

The Dutch Hospital Standardised Mortality Ratio (HSMR) method and cardiac surgery: benchmarking in a national cohort using hospital administration data versus a clinical database

PubMed Central

Siregar, S; Pouw, M E; Moons, K G M; Versteegh, M I M; Bots, M L; van der Graaf, Y; Kalkman, C J; van Herwerden, L A; Groenwold, R H H

2014-01-01

Objective To compare the accuracy of data from hospital administration databases and a national clinical cardiac surgery database and to compare the performance of the Dutch hospital standardised mortality ratio (HSMR) method and the logistic European System for Cardiac Operative Risk Evaluation, for the purpose of benchmarking of mortality across hospitals. Methods Information on all patients undergoing cardiac surgery between 1 January 2007 and 31 December 2010 in 10 centres was extracted from The Netherlands Association for Cardio-Thoracic Surgery database and the Hospital Discharge Registry. The number of cardiac surgery interventions was compared between both databases. The European System for Cardiac Operative Risk Evaluation and hospital standardised mortality ratio models were updated in the study population and compared using the C-statistic, calibration plots and the Brier-score. Results The number of cardiac surgery interventions performed could not be assessed using the administrative database as the intervention code was incorrect in 1.4–26.3%, depending on the type of intervention. In 7.3% no intervention code was registered. The updated administrative model was inferior to the updated clinical model with respect to discrimination (c-statistic of 0.77 vs 0.85, p<0.001) and calibration (Brier Score of 2.8% vs 2.6%, p<0.001, maximum score 3.0%). Two average performing hospitals according to the clinical model became outliers when benchmarking was performed using the administrative model. Conclusions In cardiac surgery, administrative data are less suitable than clinical data for the purpose of benchmarking. The use of either administrative or clinical risk-adjustment models can affect the outlier status of hospitals. Risk-adjustment models including procedure-specific clinical risk factors are recommended. PMID:24334377

Estimation and prediction under local volatility jump-diffusion model

NASA Astrophysics Data System (ADS)

Kim, Namhyoung; Lee, Younhee

2018-02-01

Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

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

PubMed

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

2013-09-01

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

A Framework for Widespread Replication of a Highly Spatially Resolved Childhood Lead Exposure Risk Model

PubMed Central

Kim, Dohyeong; Galeano, M. Alicia Overstreet; Hull, Andrew; Miranda, Marie Lynn

2008-01-01

Background Preventive approaches to childhood lead poisoning are critical for addressing this longstanding environmental health concern. Moreover, increasing evidence of cognitive effects of blood lead levels < 10 μg/dL highlights the need for improved exposure prevention interventions. Objectives Geographic information system–based childhood lead exposure risk models, especially if executed at highly resolved spatial scales, can help identify children most at risk of lead exposure, as well as prioritize and direct housing and health-protective intervention programs. However, developing highly resolved spatial data requires labor-and time-intensive geocoding and analytical processes. In this study we evaluated the benefit of increased effort spent geocoding in terms of improved performance of lead exposure risk models. Methods We constructed three childhood lead exposure risk models based on established methods but using different levels of geocoded data from blood lead surveillance, county tax assessors, and the 2000 U.S. Census for 18 counties in North Carolina. We used the results to predict lead exposure risk levels mapped at the individual tax parcel unit. Results The models performed well enough to identify high-risk areas for targeted intervention, even with a relatively low level of effort on geocoding. Conclusions This study demonstrates the feasibility of widespread replication of highly spatially resolved childhood lead exposure risk models. The models guide resource-constrained local health and housing departments and community-based organizations on how best to expend their efforts in preventing and mitigating lead exposure risk in their communities. PMID:19079729

Rapid Chemical Exposure and Dose Research

EPA Pesticide Factsheets

EPA evaluates the potential risks of the manufacture and use of thousands of chemicals. To assist with this evaluation, EPA scientists developed a rapid, automated model using off the shelf technology that predicts exposures for thousands of chemicals.

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.

Application of a degree-day model of West Nile virus transmission risk to the East Coast of the United States of America.

PubMed

Konrad, Sarah K; Miller, Scott N

2012-11-01

A geographical information systems model that identifies regions of the United States of America (USA) susceptible to West Nile virus (WNV) transmission risk is presented. This system has previously been calibrated and tested in the western USA; in this paper we use datasets of WNV-killed birds from South Carolina and Connecticut to test the model in the eastern USA. Because their response to WNV infection is highly predictable, American crows were chosen as the primary source for model calibration and testing. Where crow data are absent, other birds are shown to be an effective substitute. Model results show that the same calibrated model demonstrated to work in the western USA has the same predictive ability in the eastern USA, allowing for a continental-scale evaluation of the transmission risk of WNV at a daily time step. The calibrated model is independent of mosquito species and requires inputs of only local maximum and minimum temperatures. Of benefit to the general public and vector control districts, the model predicts the onset of seasonal transmission risk, although it is less effective at identifying the end of the transmission risk season.

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

PubMed

Pouillot, Régis; Delignette-Muller, Marie Laure

2010-09-01

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

A systematic and critical review of model-based economic evaluations of pharmacotherapeutics in patients with bipolar disorder.

PubMed

Mohiuddin, Syed

2014-08-01

Bipolar disorder (BD) is a chronic and relapsing mental illness with a considerable health-related and economic burden. The primary goal of pharmacotherapeutics for BD is to improve patients' well-being. The use of decision-analytic models is key in assessing the added value of the pharmacotherapeutics aimed at treating the illness, but concerns have been expressed about the appropriateness of different modelling techniques and about the transparency in the reporting of economic evaluations. This paper aimed to identify and critically appraise published model-based economic evaluations of pharmacotherapeutics in BD patients. A systematic review combining common terms for BD and economic evaluation was conducted in MEDLINE, EMBASE, PSYCINFO and ECONLIT. Studies identified were summarised and critically appraised in terms of the use of modelling technique, model structure and data sources. Considering the prognosis and management of BD, the possible benefits and limitations of each modelling technique are discussed. Fourteen studies were identified using model-based economic evaluations of pharmacotherapeutics in BD patients. Of these 14 studies, nine used Markov, three used discrete-event simulation (DES) and two used decision-tree models. Most of the studies (n = 11) did not include the rationale for the choice of modelling technique undertaken. Half of the studies did not include the risk of mortality. Surprisingly, no study considered the risk of having a mixed bipolar episode. This review identified various modelling issues that could potentially reduce the comparability of one pharmacotherapeutic intervention with another. Better use and reporting of the modelling techniques in the future studies are essential. DES modelling appears to be a flexible and comprehensive technique for evaluating the comparability of BD treatment options because of its greater flexibility of depicting the disease progression over time. However, depending on the research question, modelling techniques other than DES might also be appropriate in some cases.

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

EPA Science Inventory

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

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.

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

PubMed

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

2009-03-01

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

A behavioural and neural evaluation of prospective decision-making under risk

PubMed Central

Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J.

2010-01-01

Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single choice contexts there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal pre-determined strategy, irrespective of the particular order in which options are presented. An alternative model involves continuously re-evaluating decision utilities, without prescribing a specific future set of choices. Here, using behavioral and functional magnetic resonance imaging (fMRI) data, we studied human subjects in a sequential choice task and use these data to compare alternative decision models of valuation and strategy selection. We provide evidence that subjects adopt a model of re-evaluating decision utilities, where available strategies are continuously updated and combined in assessing action values. We validate this model by using simultaneously-acquired fMRI data to show that sequential choice evokes a pattern of neural response consistent with a tracking of anticipated distribution of future reward, as expected in such a model. Thus, brain activity evoked at each decision point reflects the expected mean, variance and skewness of possible payoffs, consistent with the idea that sequential choice evokes a prospective evaluation of both available strategies and possible outcomes. PMID:20980595

Economic evaluation of strategies for restarting anticoagulation therapy after a first event of unprovoked venous thromboembolism.

PubMed

Monahan, M; Ensor, J; Moore, D; Fitzmaurice, D; Jowett, S

2017-08-01

Essentials Correct duration of treatment after a first unprovoked venous thromboembolism (VTE) is unknown. We assessed when restarting anticoagulation was worthwhile based on patient risk of recurrent VTE. When the risk over a one-year period is 17.5%, restarting is cost-effective. However, sensitivity analyses indicate large uncertainty in the estimates. Background Following at least 3 months of anticoagulation therapy after a first unprovoked venous thromboembolism (VTE), there is uncertainty about the duration of therapy. Further anticoagulation therapy reduces the risk of having a potentially fatal recurrent VTE but at the expense of a higher risk of bleeding, which can also be fatal. Objective An economic evaluation sought to estimate the long-term cost-effectiveness of using a decision rule for restarting anticoagulation therapy vs. no extension of therapy in patients based on their risk of a further unprovoked VTE. Methods A Markov patient-level simulation model was developed, which adopted a lifetime time horizon with monthly time cycles and was from a UK National Health Service (NHS)/Personal Social Services (PSS) perspective. Results Base-case model results suggest that treating patients with a predicted 1 year VTE risk of 17.5% or higher may be cost-effective if decision makers are willing to pay up to £20 000 per quality adjusted life year (QALY) gained. However, probabilistic sensitivity analysis shows that the model was highly sensitive to overall parameter uncertainty and caution is warranted in selecting the optimal decision rule on cost-effectiveness grounds. Univariate sensitivity analyses indicate variables such as anticoagulation therapy disutility and mortality risks were very influential in driving model results. Conclusion This represents the first economic model to consider the use of a decision rule for restarting therapy for unprovoked VTE patients. Better data are required to predict long-term bleeding risks during therapy in this patient group. © 2017 International Society on Thrombosis and Haemostasis.

The Lack of Utility of Circulating Biomarkers of Inflammation and Endothelial Dysfunction for Type 2 Diabetes Risk Prediction Among Postmenopausal Women

PubMed Central

Chao, Chun; Song, Yiqing; Cook, Nancy; Tseng, Chi-Hong; Manson, JoAnn E.; Eaton, Charles; Margolis, Karen L.; Rodriguez, Beatriz; Phillips, Lawrence S.; Tinker, Lesley F.; Liu, Simin

2011-01-01

Background Recent studies have linked plasma markers of inflammation and endothelial dysfunction to type 2 diabetes mellitus (DM) development. However, the utility of these novel biomarkers for type 2 DM risk prediction remains uncertain. Methods The Women’s Health Initiative Observational Study (WHIOS), a prospective cohort, and a nested case-control study within the WHIOS of 1584 incident type 2 DM cases and 2198 matched controls were used to evaluate the utility of plasma markers of inflammation and endothelial dysfunction for type 2 DM risk prediction. Between September 1994 and December 1998, 93 676 women aged 50 to 79 years were enrolled in the WHIOS. Fasting plasma levels of glucose, insulin, white blood cells, tumor necrosis factor receptor 2, interleukin 6, high-sensitivity C-reactive protein, E-selectin, soluble intercellular adhesion molecule 1, and vascular cell adhesion molecule 1 were measured using blood samples collected at baseline. A series of prediction models including traditional risk factors and novel plasma markers were evaluated on the basis of global model fit, model discrimination, net reclassification improvement, and positive and negative predictive values. Results Although white blood cell count and levels of interleukin 6, high-sensitivity C-reactive protein, and soluble intercellular adhesion molecule 1 significantly enhanced model fit, none of the inflammatory and endothelial dysfunction markers improved the ability of model discrimination (area under the receiver operating characteristic curve, 0.93 vs 0.93), net reclassification, or predictive values (positive, 0.22 vs 0.24; negative, 0.99 vs 0.99 [using 15% 6-year type 2 DM risk as the cutoff]) compared with traditional risk factors. Similar results were obtained in ethnic-specific analyses. Conclusion Beyond traditional risk factors, measurement of plasma markers of systemic inflammation and endothelial dysfunction contribute relatively little additional value in clinical type 2 DM risk prediction in a multiethnic cohort of postmenopausal women. PMID:20876407

Application of a prediction model for work-related sensitisation in bakery workers.

PubMed

Meijer, E; Suarthana, E; Rooijackers, J; Grobbee, D E; Jacobs, J H; Meijster, T; de Monchy, J G R; van Otterloo, E; van Rooy, F G B G J; Spithoven, J J G; Zaat, V A C; Heederik, D J J

2010-10-01

Identification of work-related allergy, particularly work-related asthma, in a (nationwide) medical surveillance programme among bakery workers requires an effective and efficient strategy. Bakers at high risk of having work-related allergy were indentified by use of a questionnaire-based prediction model for work-related sensitisation. The questionnaire was applied among 5,325 participating bakers. Sequential diagnostic investigations were performed only in those with an elevated risk. Performance of the model was evaluated in 674 randomly selected bakers who participated in the medical surveillance programme and the validation study. Clinical investigations were evaluated in the first 73 bakers referred at high risk. Overall 90% of bakers at risk of having asthma could be identified. Individuals at low risk showed 0.3-3.8% work-related respiratory symptoms, medication use or absenteeism. Predicting flour sensitisation by a simple questionnaire and score chart seems more effective at detecting work-related allergy than serology testing followed by clinical investigation in all immunoglobulin E class II-positive individuals. This prediction based stratification procedure appeared effective in detecting work-related allergy among bakers and can accurately be used for periodic examination, especially in small enterprises where delivery of adequate care is difficult. This approach may contribute to cost reduction.

On the practice of ignoring center-patient interactions in evaluating hospital performance.

PubMed

Varewyck, Machteld; Vansteelandt, Stijn; Eriksson, Marie; Goetghebeur, Els

2016-01-30

We evaluate the performance of medical centers based on a continuous or binary patient outcome (e.g., 30-day mortality). Common practice adjusts for differences in patient mix through outcome regression models, which include patient-specific baseline covariates (e.g., age and disease stage) besides center effects. Because a large number of centers may need to be evaluated, the typical model postulates that the effect of a center on outcome is constant over patient characteristics. This may be violated, for example, when some centers are specialized in children or geriatric patients. Including interactions between certain patient characteristics and the many fixed center effects in the model increases the risk for overfitting, however, and could imply a loss of power for detecting centers with deviating mortality. Therefore, we assess how the common practice of ignoring such interactions impacts the bias and precision of directly and indirectly standardized risks. The reassuring conclusion is that the common practice of working with the main effects of a center has minor impact on hospital evaluation, unless some centers actually perform substantially better on a specific group of patients and there is strong confounding through the corresponding patient characteristic. The bias is then driven by an interplay of the relative center size, the overlap between covariate distributions, and the magnitude of the interaction effect. Interestingly, the bias on indirectly standardized risks is smaller than on directly standardized risks. We illustrate our findings by simulation and in an analysis of 30-day mortality on Riksstroke. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

How Does Ambient Air Temperature Affect Diabetes Mortality in Tropical Cities?

PubMed Central

Seposo, Xerxes T.; Dang, Tran Ngoc; Honda, Yasushi

2017-01-01

Diabetes is well-known as one of the many chronic diseases that affect different age groups. Currently, most studies that evaluated the effects of temperature on diabetes mortality focused on temperate and subtropical settings, but no study has been conducted to assess the relationship in a tropical setting. We conducted the first multi-city study carried out in tropical cities, which evaluated the temperature–diabetes relationship. We collected daily diabetes mortality (ICD E10–E14) of four Philippine cities from 2006 to 2011. Same period meteorological data were obtained from the National Oceanic and Atmospheric Administration. We used a generalized additive model coupled with a distributed lag non-linear model (DLNM) in determining the relative risks. Results showed that both low and high temperatures pose greater risks among diabetics. Likewise, the study was able to observe the: (1) high risk brought about by low temperature, aside from the largely observed high risks by high temperature; and (2) protective effects in low temperature percentile. These results provide significant policy implications with strategies related to diabetes risk groups in relation to health service and care strategies. PMID:28379204

How Does Ambient Air Temperature Affect Diabetes Mortality in Tropical Cities?

PubMed

Seposo, Xerxes T; Dang, Tran Ngoc; Honda, Yasushi

2017-04-05

Diabetes is well-known as one of the many chronic diseases that affect different age groups. Currently, most studies that evaluated the effects of temperature on diabetes mortality focused on temperate and subtropical settings, but no study has been conducted to assess the relationship in a tropical setting. We conducted the first multi-city study carried out in tropical cities, which evaluated the temperature-diabetes relationship. We collected daily diabetes mortality (ICD E10-E14) of four Philippine cities from 2006 to 2011. Same period meteorological data were obtained from the National Oceanic and Atmospheric Administration. We used a generalized additive model coupled with a distributed lag non-linear model (DLNM) in determining the relative risks. Results showed that both low and high temperatures pose greater risks among diabetics. Likewise, the study was able to observe the: (1) high risk brought about by low temperature, aside from the largely observed high risks by high temperature; and (2) protective effects in low temperature percentile. These results provide significant policy implications with strategies related to diabetes risk groups in relation to health service and care strategies.

A case-control study of rheumatoid arthritis revealed abdominal obesity and environmental risk factor interactions in northern China.

PubMed

Fu, Lingyu; Zhang, Jianming; Jin, Lei; Zhang, Yao; Cui, Saisai; Chen, Meng

2018-03-01

The aim of this study was to evaluate new and previously hypothesized environmental risk factors and their interaction with rheumatoid arthritis (RA). Four hundred patients recently diagnosed with RA and 400 controls frequency-matched by gender and birth year using Propensity Score Matching (PSM) were selected from northern China. Investigation was performed using self-reported data from interviewer-administered surveys. Associations between exposure variables and risk of RA were evaluated using multifactor non-conditional logistic regression. It showed that damp localities, draft indoor, abdominal obesity (AO), and family history of RA among first-degree relatives were independent risk factors and drinking of milk was independent protective factors for RA. Besides these risk factors, in women, infrequent delivery times, early age at menopause, and late age at menarche were also independent risk factors for RA. Both the additive model and the multiplication model suggested that there was an interaction relationship between AO and damp localities (p < .001), and only the additive model suggested that there was interaction relationship between AO and no milk drinking (p < .001) in our study population. In women, there was interaction relationship between AO and damp localities (p < .001) and between AO and age at menopause (p < .001). In northern China, damp localities, draft indoor, AO, family history of RA among first-degree relatives, and no milk drinking may be important risk factors of RA patients.

Natural hazard modeling and uncertainty analysis [Chapter 2

Treesearch

Matthew Thompson; Jord J. Warmink

2017-01-01

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

Chapter 7: Description of MISCAN-lung, the Erasmus MC Lung Cancer microsimulation model for evaluating cancer control interventions.

PubMed

Schultz, F W; Boer, R; de Koning, H J

2012-07-01

The MISCAN-lung model was designed to simulate population trends in lung cancer (LC) for comprehensive surveillance of the disease, to relate past exposure to risk factors to (observed) LC incidence and mortality, and to estimate the impact of cancer-control interventions. MISCAN-lung employs the technique of stochastic microsimulation of life histories affected by risk factors. It includes the two-stage clonal expansion model for carcinogenesis and a detailed LC progression model; the latter is specifically intended for the evaluation of screenings. This article elucidates further the principles of MISCAN-lung and describes its application to a comparative study within the CISNET Lung Working Group on the impact of tobacco control on U.S. LC mortality. MISCAN-lung yields an estimate of the number of LC deaths avoided during 1975-2000. The potential number of avoidable LC deaths, had everybody quit smoking in 1965, is 2.2 million; 750,000 deaths (30%) were avoided in the United States due to actual tobacco control interventions. The model fits in the actual tobacco-control scenario, providing credibility to the estimates of other scenarios, although considering survey-reported smoking trends alone has limitations. © 2012 Society for Risk Analysis.

Clinical Validity, Understandability, and Actionability of Online Cardiovascular Disease Risk Calculators: Systematic Review.

PubMed

Bonner, Carissa; Fajardo, Michael Anthony; Hui, Samuel; Stubbs, Renee; Trevena, Lyndal

2018-02-01

Online health information is particularly important for cardiovascular disease (CVD) prevention, where lifestyle changes are recommended until risk becomes high enough to warrant pharmacological intervention. Online information is abundant, but the quality is often poor and many people do not have adequate health literacy to access, understand, and use it effectively. This project aimed to review and evaluate the suitability of online CVD risk calculators for use by low health literate consumers in terms of clinical validity, understandability, and actionability. This systematic review of public websites from August to November 2016 used evaluation of clinical validity based on a high-risk patient profile and assessment of understandability and actionability using Patient Education Material Evaluation Tool for Print Materials. A total of 67 unique webpages and 73 unique CVD risk calculators were identified. The same high-risk patient profile produced widely variable CVD risk estimates, ranging from as little as 3% to as high as a 43% risk of a CVD event over the next 10 years. One-quarter (25%) of risk calculators did not specify what model these estimates were based on. The most common clinical model was Framingham (44%), and most calculators (77%) provided a 10-year CVD risk estimate. The calculators scored moderately on understandability (mean score 64%) and poorly on actionability (mean score 19%). The absolute percentage risk was stated in most (but not all) calculators (79%), and only 18% included graphical formats consistent with recommended risk communication guidelines. There is a plethora of online CVD risk calculators available, but they are not readily understandable and their actionability is poor. Entering the same clinical information produces widely varying results with little explanation. Developers need to address actionability as well as clinical validity and understandability to improve usefulness to consumers with low health literacy. ©Carissa Bonner, Michael Anthony Fajardo, Samuel Hui, Renee Stubbs, Lyndal Trevena. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 01.02.2018.

Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units.

PubMed

Shahin, Jason; Allen, Elizabeth J; Patel, Krishna; Muskett, Hannah; Harvey, Sheila E; Edgeworth, Jonathan; Kibbler, Christopher C; Barnes, Rosemary A; Biswas, Sharmistha; Soni, Neil; Rowan, Kathryn M; Harrison, David A

2016-09-09

Given the predominance of invasive fungal disease (IFD) amongst the non-immunocompromised adult critically ill population, the potential benefit of antifungal prophylaxis and the lack of generalisable tools to identify high risk patients, the aim of the current study was to describe the epidemiology of IFD in UK critical care units, and to develop and validate a clinical risk prediction tool to identify non-neutropenic, critically ill adult patients at high risk of IFD who would benefit from antifungal prophylaxis. Data on risk factors for, and outcomes from, IFD were collected for consecutive admissions to adult, general critical care units in the UK participating in the Fungal Infection Risk Evaluation (FIRE) Study. Three risk prediction models were developed to model the risk of subsequent Candida IFD based on information available at three time points: admission to the critical care unit, at the end of 24 h and at the end of calendar day 3 of the critical care unit stay. The final model at each time point was evaluated in the three external validation samples. Between July 2009 and April 2011, 60,778 admissions from 96 critical care units were recruited. In total, 359 admissions (0.6 %) were admitted with, or developed, Candida IFD (66 % Candida albicans). At the rate of candidaemia of 3.3 per 1000 admissions, blood was the most common Candida IFD infection site. Of the initial 46 potential variables, the final admission model and the 24-h model both contained seven variables while the end of calendar day 3 model contained five variables. The end of calendar day 3 model performed the best with a c index of 0.709 in the full validation sample. Incidence of Candida IFD in UK critical care units in this study was consistent with reports from other European epidemiological studies, but lower than that suggested by previous hospital-wide surveillance in the UK during the 1990s. Risk modeling using classical statistical methods produced relatively simple risk models, and associated clinical decision rules, that provided acceptable discrimination for identifying patients at 'high risk' of Candida IFD. The FIRE Study was reviewed and approved by the Bolton NHS Research Ethics Committee (reference: 08/H1009/85), the Scotland A Research Ethics Committee (reference: 09/MRE00/76) and the National Information Governance Board (approval number: PIAG 2-10(f)/2005).

Carotid Artery End-Diastolic Velocity and Future Cerebro-Cardiovascular Events in Asymptomatic High Risk Patients.

PubMed

Chung, Hyemoon; Jung, Young Hak; Kim, Ki-Hyun; Kim, Jong-Youn; Min, Pil-Ki; Yoon, Young Won; Lee, Byoung Kwon; Hong, Bum-Kee; Rim, Se-Joong; Kwon, Hyuck Moon; Choi, Eui-Young

2016-01-01

Prognostic value of additional carotid Doppler evaluations to carotid intima-media thickness (IMT) and plaque has not been completely evaluated. A total of 1119 patients with risk factors for, but without, overt coronary artery disease (CAD), who underwent both carotid ultrasound and Doppler examination were included in the present study. Parameters of interest included peak systolic and end-diastolic velocities, resistive indices of the carotid arteries, IMT, and plaque measurements. The primary end-point was all-cause cerebro-cardiovascular events (CVEs) including acute myocardial infarction, coronary revascularization therapy, heart failure admission, stroke, and cardiovascular death. Model 1 covariates comprised age and sex; Model 2 also included hypertension, diabetes and smoking; Model 3 also had use of aspirin and statin; and Model 4 also included IMT and plaque. The mean follow-up duration was 1386±461 days and the mean age of the study population was 60±12 years. Amongst 1119 participants, 43% were women, 57% had a history of hypertension, and 23% had diabetes. During follow-up, 6.6% of patients experienced CVEs. Among carotid Doppler parameters, average common carotid artery end-diastolic velocity was the independent predictor for future CVEs after adjustments for all models variables (HR 0.95 per cm/s, 95% confident interval 0.91-0.99, p=0.034 in Model 4) and significantly increased the predictive value of Model 4 (global χ(2)=59.0 vs. 62.8, p=0.029). Carotid Doppler measurements in addition to IMT and plaque evaluation are independently associated with future CVEs in asymptomatic patients at risk for CAD.

[Cardiovascular risk in Spanish smokers compared to non-smokers: RETRATOS study].

PubMed

Fernández de Bobadilla, Jaime; Sanz de Burgoa, Verónica; Garrido Morales, Patricio; López de Sá, Esteban

2011-11-01

To evaluate the level of cardiovascular risk in smokers seenin Primary Care clinics. Epidemiologic, cross-sectional and multicentre study. Primary Care. Every investigator included 4 consecutive patients (3 smokers, 1 non-smoker) aged 35-50 years, who came to the clinic for any reason. A total of 2,184 patients were included; 2,124 (1,597 smokers; 527 non-smokers) were evaluated and 60 patients were excluded because they did not meet with selection criteria. The 10-year risk of suffering from a fatal cardiovascular disease (CVDR) was calculated according to the SCORE (Systematic Coronary Risk Evaluation) model. The 10-year lethal CVR according SCORE model, was classified as: very high (> 15%), high (10-14%), slightly high (5-9%), average (3-4%), low (2%), very low (1%) and negligible (< 1%). A logistical regression model was used to estimate the relationship between smoking and prior cardiovascular events. 10-year fatal CVDR according to the SCORE model was significantly higher in smokers (40±5.3) vs. non-smokers (1.9±2.5) (P<.0001). low (< 3%) [78.0% non-smokers vs. 60.7% smokers (P<.0001)]; intermediate (3-5%) [11.1% non-smokers vs. 12.6% smokers (P<.001)]; high (> 5%) [10.9% non-smokers vs. 26.7% smokers (P<.001)]. The logistical regression model showed that non-smokers vs. smokers had less probability of suffering myocardial infarction (OR 0.3; 95% confidence interval (95% CI): 0.1-0.8; P<.0001), peripheral vascular disease (OR 0.6; 95% CI: 0.4-1.0; P=.0180) and chronic obstructive lung disease (OR 0.18; 95% CI: 0.1-0.2; P=.0507). Smoking is related to a high risk of fatal cardiovascular disease. Active promotion in Primary Care clinics of measures aimed at reducing the prevalence of the smoking habit would lead to a lowering of cardiovascular morbidity and mortality. Copyright © 2010 Elsevier España, S.L. All rights reserved.

Experimental models of tracheobronchial stenoses: a useful tool for evaluating airway stents.

PubMed

Marquette, C H; Mensier, E; Copin, M C; Desmidt, A; Freitag, L; Witt, C; Petyt, L; Ramon, P

1995-09-01

Stent implantation is a conservative alternative to open operation for treating benign tracheobronchial strictures. Most of the presently available stents were primarily designed for endovascular use. Their respiratory use entails a risk of iatrogenic complications. From a scientific and from an ethical point of view these risks justify preclinical evaluation of new respiratory stents in experimental models of central airway stenoses. Therefore, an attempt was made to develop such models in piglets and adult minipigs. Tracheal stenoses were obtained by creating first a segmental tracheomalacia through extramucosal resection of cartilaginous arches. The fibrous component of the stenoses was then obtained through bronchoscopic application of a caustic agent causing progressive deep mucosal and submucosal injury. Stenoses of the main bronchi were created by topical application of the caustic agent only. These models demonstrated the typical features of benign fibromalacic tracheobronchial stenoses with constant recurrence after mechanical dilation. Preliminary experiments showed that short-term problems of tolerance of stent prototypes are easily demonstrable in these models. These experimental models, which simulate quite realistically human diseases, offer the opportunity to perfect new tracheobronchial stents specifically designed for respiratory use and to evaluate their long-term tolerance before their use in humans.

Identifying crash-prone traffic conditions under different weather on freeways.

PubMed

Xu, Chengcheng; Wang, Wei; Liu, Pan

2013-09-01

Understanding the relationships between traffic flow characteristics and crash risk under adverse weather conditions will help highway agencies develop proactive safety management strategies to improve traffic safety in adverse weather conditions. The primary objective is to develop separate crash risk prediction models for different weather conditions. The crash data, weather data, and traffic data used in this study were collected on the I-880N freeway in California in 2008 and 2010. This study considered three different weather conditions: clear weather, rainy weather, and reduced visibility weather. The preliminary analysis showed that there was some heterogeneity in the risk estimates for traffic flow characteristics by weather conditions, and that the crash risk prediction model for all weather conditions cannot capture the impacts of the traffic flow variables on crash risk under adverse weather conditions. The Bayesian random intercept logistic regression models were applied to link the likelihood of crash occurrence with various traffic flow characteristics under different weather conditions. The crash risk prediction models were compared to their corresponding logistic regression model. It was found that the random intercept model improved the goodness-of-fit of the crash risk prediction models. The model estimation results showed that the traffic flow characteristics contributing to crash risk were different across different weather conditions. The speed difference between upstream and downstream stations was found to be significant in each crash risk prediction model. Speed difference between upstream and downstream stations had the largest impact on crash risk in reduced visibility weather, followed by that in rainy weather. The ROC curves were further developed to evaluate the predictive performance of the crash risk prediction models under different weather conditions. The predictive performance of the crash risk model for clear weather was better than those of the crash risk models for adverse weather conditions. The research results could promote a better understanding of the impacts of traffic flow characteristics on crash risk under adverse weather conditions, which will help transportation professionals to develop better crash prevention strategies in adverse weather. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Transformation in the pharmaceutical industry: transformation-induced quality risks--a survey.

PubMed

Shafiei, Nader; Ford, James L; Morecroft, Charles W; Lisboa, Paulo J; Taylor, Mark J; Mouzughi, Yusra

2013-01-01

This paper is the fourth in a series that explores ongoing transformation in the pharmaceutical industry and its impact on pharmaceutical quality from the perspective of risk identification. The aim of this paper is to validate proposed quality risks through elicitation of expert opinion and define the resultant quality risk model. Expert opinion was obtained using a questionnaire-based survey with participants with recognized expertise in pharmaceutical regulation, product lifecycle, or technology. The results of the survey validate the theoretical and operational evidence in support of the four main pharmaceutical transformation triggers previously identified. The quality risk model resulting from the survey indicated a firm relationship between the pharmaceutical quality risks and regulatory compliance outcomes during the marketing approval and post-marketing phases of the product lifecycle and a weaker relationship during the pre-market evaluation phase. In this paper through conduct of an expert opinion survey the proposed quality risks carried forward from an earlier part of the research are validated and resultant quality risk model is defined. The survey results validate the theoretical and operational evidence previously identified. The quality risk model indicates that transformation-related risks have a larger regulatory compliance impact during product approval, manufacturing, distribution, and commercial use than during the development phase.

Early identification of patients at risk of acute lung injury: evaluation of lung injury prediction score in a multicenter cohort study.

PubMed

Gajic, Ognjen; Dabbagh, Ousama; Park, Pauline K; Adesanya, Adebola; Chang, Steven Y; Hou, Peter; Anderson, Harry; Hoth, J Jason; Mikkelsen, Mark E; Gentile, Nina T; Gong, Michelle N; Talmor, Daniel; Bajwa, Ednan; Watkins, Timothy R; Festic, Emir; Yilmaz, Murat; Iscimen, Remzi; Kaufman, David A; Esper, Annette M; Sadikot, Ruxana; Douglas, Ivor; Sevransky, Jonathan; Malinchoc, Michael

2011-02-15

Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies. To determine the frequency and outcome of ALI development in patients at risk and validate a lung injury prediction score (LIPS). In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions. Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1-4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78-0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9-5.7). ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT00889772).

Cardio-respiratory outcomes associated with exposure to wildfire smoke are modified by measures of community health

PubMed Central

2012-01-01

Background Characterizing factors which determine susceptibility to air pollution is an important step in understanding the distribution of risk in a population and is critical for setting appropriate policies. We evaluate general and specific measures of community health as modifiers of risk for asthma and congestive heart failure following an episode of acute exposure to wildfire smoke. Methods A population-based study of emergency department visits and daily concentrations of fine particulate matter during a wildfire in North Carolina was performed. Determinants of community health defined by County Health Rankings were evaluated as modifiers of the relative risk. A total of 40 mostly rural counties were included in the study. These rankings measure factors influencing health: health behaviors, access and quality of clinical care, social and economic factors, and physical environment, as well as, the outcomes of health: premature mortality and morbidity. Pollutant concentrations were obtained from a mathematically modeled smoke forecasting system. Estimates of relative risk for emergency department visits were based on Poisson mixed effects regression models applied to daily visit counts. Results For asthma, the strongest association was observed at lag day 0 with excess relative risk of 66%(28,117). For congestive heart failure the excess relative risk was 42%(5,93). The largest difference in risk was observed after stratifying on the basis of Socio-Economic Factors. Difference in risk between bottom and top ranked counties by Socio-Economic Factors was 85% and 124% for asthma and congestive heart failure respectively. Conclusions The results indicate that Socio-Economic Factors should be considered as modifying risk factors in air pollution studies and be evaluated in the assessment of air pollution impacts. PMID:23006928

Cardio-respiratory outcomes associated with exposure to wildfire smoke are modified by measures of community health.

PubMed

Rappold, Ana G; Cascio, Wayne E; Kilaru, Vasu J; Stone, Susan L; Neas, Lucas M; Devlin, Robert B; Diaz-Sanchez, David

2012-09-24

Characterizing factors which determine susceptibility to air pollution is an important step in understanding the distribution of risk in a population and is critical for setting appropriate policies. We evaluate general and specific measures of community health as modifiers of risk for asthma and congestive heart failure following an episode of acute exposure to wildfire smoke. A population-based study of emergency department visits and daily concentrations of fine particulate matter during a wildfire in North Carolina was performed. Determinants of community health defined by County Health Rankings were evaluated as modifiers of the relative risk. A total of 40 mostly rural counties were included in the study. These rankings measure factors influencing health: health behaviors, access and quality of clinical care, social and economic factors, and physical environment, as well as, the outcomes of health: premature mortality and morbidity. Pollutant concentrations were obtained from a mathematically modeled smoke forecasting system. Estimates of relative risk for emergency department visits were based on Poisson mixed effects regression models applied to daily visit counts. For asthma, the strongest association was observed at lag day 0 with excess relative risk of 66% (28,117). For congestive heart failure the excess relative risk was 42% (5,93). The largest difference in risk was observed after stratifying on the basis of Socio-Economic Factors. Difference in risk between bottom and top ranked counties by Socio-Economic Factors was 85% and 124% for asthma and congestive heart failure respectively. The results indicate that Socio-Economic Factors should be considered as modifying risk factors in air pollution studies and be evaluated in the assessment of air pollution impacts.

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

EPA Science Inventory

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

Integrating Human Factors into Space Vehicle Processing for Risk Management

NASA Technical Reports Server (NTRS)

Woodbury, Sarah; Richards, Kimberly J.

2008-01-01

This presentation will discuss the multiple projects performed in United Space Alliance's Human Engineering Modeling and Performance (HEMAP) Lab, improvements that resulted from analysis, and the future applications of the HEMAP Lab for risk assessment by evaluating human/machine interaction and ergonomic designs.

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 Missing Stakeholder Group: Why Patients Should be Involved in Health Economic Modelling.

PubMed

van Voorn, George A K; Vemer, Pepijn; Hamerlijnck, Dominique; Ramos, Isaac Corro; Teunissen, Geertruida J; Al, Maiwenn; Feenstra, Talitha L

2016-04-01

Evaluations of healthcare interventions, e.g. new drugs or other new treatment strategies, commonly include a cost-effectiveness analysis (CEA) that is based on the application of health economic (HE) models. As end users, patients are important stakeholders regarding the outcomes of CEAs, yet their knowledge of HE model development and application, or their involvement therein, is absent. This paper considers possible benefits and risks of patient involvement in HE model development and application for modellers and patients. An exploratory review of the literature has been performed on stakeholder-involved modelling in various disciplines. In addition, Dutch patient experts have been interviewed about their experience in, and opinion about, the application of HE models. Patients have little to no knowledge of HE models and are seldom involved in HE model development and application. Benefits of becoming involved would include a greater understanding and possible acceptance by patients of HE model application, improved model validation, and a more direct infusion of patient expertise. Risks would include patient bias and increased costs of modelling. Patient involvement in HE modelling seems to carry several benefits as well as risks. We claim that the benefits may outweigh the risks and that patients should become involved.

Risk Modeling of Interdependent Complex Systems of Systems: Theory and Practice.

PubMed

Haimes, Yacov Y

2018-01-01

The emergence of the complexity characterizing our systems of systems (SoS) requires a reevaluation of the way we model, assess, manage, communicate, and analyze the risk thereto. Current models for risk analysis of emergent complex SoS are insufficient because too often they rely on the same risk functions and models used for single systems. These models commonly fail to incorporate the complexity derived from the networks of interdependencies and interconnectedness (I-I) characterizing SoS. There is a need to reevaluate currently practiced risk analysis to respond to this reality by examining, and thus comprehending, what makes emergent SoS complex. The key to evaluating the risk to SoS lies in understanding the genesis of characterizing I-I of systems manifested through shared states and other essential entities within and among the systems that constitute SoS. The term "essential entities" includes shared decisions, resources, functions, policies, decisionmakers, stakeholders, organizational setups, and others. This undertaking can be accomplished by building on state-space theory, which is fundamental to systems engineering and process control. This article presents a theoretical and analytical framework for modeling the risk to SoS with two case studies performed with the MITRE Corporation and demonstrates the pivotal contributions made by shared states and other essential entities to modeling and analysis of the risk to complex SoS. A third case study highlights the multifarious representations of SoS, which require harmonizing the risk analysis process currently applied to single systems when applied to complex SoS. © 2017 Society for Risk Analysis.

A New Scoring System to Predict the Risk for High-risk Adenoma and Comparison of Existing Risk Calculators.

PubMed

Murchie, Brent; Tandon, Kanwarpreet; Hakim, Seifeldin; Shah, Kinchit; O'Rourke, Colin; Castro, Fernando J

2017-04-01

Colorectal cancer (CRC) screening guidelines likely over-generalizes CRC risk, 35% of Americans are not up to date with screening, and there is growing incidence of CRC in younger patients. We developed a practical prediction model for high-risk colon adenomas in an average-risk population, including an expanded definition of high-risk polyps (≥3 nonadvanced adenomas), exposing higher than average-risk patients. We also compared results with previously created calculators. Patients aged 40 to 59 years, undergoing first-time average-risk screening or diagnostic colonoscopies were evaluated. Risk calculators for advanced adenomas and high-risk adenomas were created based on age, body mass index, sex, race, and smoking history. Previously established calculators with similar risk factors were selected for comparison of concordance statistic (c-statistic) and external validation. A total of 5063 patients were included. Advanced adenomas, and high-risk adenomas were seen in 5.7% and 7.4% of the patient population, respectively. The c-statistic for our calculator was 0.639 for the prediction of advanced adenomas, and 0.650 for high-risk adenomas. When applied to our population, all previous models had lower c-statistic results although one performed similarly. Our model compares favorably to previously established prediction models. Age and body mass index were used as continuous variables, likely improving the c-statistic. It also reports absolute predictive probabilities of advanced and high-risk polyps, allowing for more individualized risk assessment of CRC.

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.

A nomogram based on mammary ductoscopic indicators for evaluating the risk of breast cancer in intraductal neoplasms with nipple discharge.

PubMed

Lian, Zhen-Qiang; Wang, Qi; Zhang, An-Qin; Zhang, Jiang-Yu; Han, Xiao-Rong; Yu, Hai-Yun; Xie, Si-Mei

2015-04-01

Mammary ductoscopy (MD) is commonly used to detect intraductal lesions associated with nipple discharge. This study investigated the relationships between ductoscopic image-based indicators and breast cancer risk, and developed a nomogram for evaluating breast cancer risk in intraductal neoplasms with nipple discharge. A total of 879 consecutive inpatients (916 breasts) with nipple discharge who underwent selective duct excision for intraductal neoplasms detected by MD from June 2008 to April 2014 were analyzed retrospectively. A nomogram was developed using a multivariate logistic regression model based on data from a training set (687 cases) and validated in an independent validation set (229 cases). A Youden-derived cut-off value was assigned to the nomogram for the diagnosis of breast cancer. Color of discharge, location, appearance, and surface of neoplasm, and morphology of ductal wall were independent predictors for breast cancer in multivariate logistic regression analysis. A nomogram based on these predictors performed well. The P value of the Hosmer-Lemeshow test for the prediction model was 0.36. Area under the curve values of 0.812 (95 % confidence interval (CI) 0.763-0.860) and 0.738 (95 % CI 0.635-0.841) was obtained in the training and validation sets, respectively. The accuracies of the nomogram for breast cancer diagnosis were 71.2 % in the training set and 75.5 % in the validation set. We developed a nomogram for evaluating breast cancer risk in intraductal neoplasms with nipple discharge based on MD image findings. This model may aid individual risk assessment and guide treatment in clinical practice.

Health effects models for nuclear power plant accident consequence analysis: Low LET radiation

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

Evans, J.S.

1990-01-01

This report describes dose-response models intended to be used in estimating the radiological health effects of nuclear power plant accidents. Models of early and continuing effects, cancers and thyroid nodules, and genetic effects are provided. Weibull dose-response functions are recommended for evaluating the risks of early and continuing health effects. Three potentially lethal early effects -- the hematopoietic, pulmonary, and gastrointestinal syndromes -- are considered. In addition, models are included for assessing the risks of several nonlethal early and continuing effects -- including prodromal vomiting and diarrhea, hypothyroidism and radiation thyroiditis, skin burns, reproductive effects, and pregnancy losses. Linear andmore » linear-quadratic models are recommended for estimating cancer risks. Parameters are given for analyzing the risks of seven types of cancer in adults -- leukemia, bone, lung, breast, gastrointestinal, thyroid, and other.'' The category, other'' cancers, is intended to reflect the combined risks of multiple myeloma, lymphoma, and cancers of the bladder, kidney, brain, ovary, uterus and cervix. Models of childhood cancers due to in utero exposure are also developed. For most cancers, both incidence and mortality are addressed. The models of cancer risk are derived largely from information summarized in BEIR III -- with some adjustment to reflect more recent studies. 64 refs., 18 figs., 46 tabs.« less

Geographic profiling to assess the risk of rare plant poaching in natural areas

USGS Publications Warehouse

Young, J.A.; Van Manen, F.T.; Thatcher, C.A.

2011-01-01

We demonstrate the use of an expert-assisted spatial model to examine geographic factors influencing the poaching risk of a rare plant (American ginseng, Panax quinquefolius L.) in Shenandoah National Park, Virginia, USA. Following principles of the analytic hierarchy process (AHP), we identified a hierarchy of 11 geographic factors deemed important to poaching risk and requested law enforcement personnel of the National Park Service to rank those factors in a series of pair-wise comparisons. We used those comparisons to determine statistical weightings of each factor and combined them into a spatial model predicting poaching risk. We tested the model using 69 locations of previous poaching incidents recorded by law enforcement personnel. These locations occurred more frequently in areas predicted by the model to have a higher risk of poaching than random locations. The results of our study can be used to evaluate resource protection strategies and to target law enforcement activities. ?? Springer Science+Business Media, LLC (outside the USA) 2011.

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

PubMed

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

2017-05-01

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

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

PubMed Central

Parker, Joel S.; Mullins, Michael; Cheang, Maggie C.U.; Leung, Samuel; Voduc, David; Vickery, Tammi; Davies, Sherri; Fauron, Christiane; He, Xiaping; Hu, Zhiyuan; Quackenbush, John F.; Stijleman, Inge J.; Palazzo, Juan; Marron, J.S.; Nobel, Andrew B.; Mardis, Elaine; Nielsen, Torsten O.; Ellis, Matthew J.; Perou, Charles M.; Bernard, Philip S.

2009-01-01

Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. Conclusion Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy. PMID:19204204

Evaluating performance of risk identification methods through a large-scale simulation of observational data.

PubMed

Ryan, Patrick B; Schuemie, Martijn J

2013-10-01

There has been only limited evaluation of statistical methods for identifying safety risks of drug exposure in observational healthcare data. Simulations can support empirical evaluation, but have not been shown to adequately model the real-world phenomena that challenge observational analyses. To design and evaluate a probabilistic framework (OSIM2) for generating simulated observational healthcare data, and to use this data for evaluating the performance of methods in identifying associations between drug exposure and health outcomes of interest. Seven observational designs, including case-control, cohort, self-controlled case series, and self-controlled cohort design were applied to 399 drug-outcome scenarios in 6 simulated datasets with no effect and injected relative risks of 1.25, 1.5, 2, 4, and 10, respectively. Longitudinal data for 10 million simulated patients were generated using a model derived from an administrative claims database, with associated demographics, periods of drug exposure derived from pharmacy dispensings, and medical conditions derived from diagnoses on medical claims. Simulation validation was performed through descriptive comparison with real source data. Method performance was evaluated using Area Under ROC Curve (AUC), bias, and mean squared error. OSIM2 replicates prevalence and types of confounding observed in real claims data. When simulated data are injected with relative risks (RR) ≥ 2, all designs have good predictive accuracy (AUC > 0.90), but when RR < 2, no methods achieve 100 % predictions. Each method exhibits a different bias profile, which changes with the effect size. OSIM2 can support methodological research. Results from simulation suggest method operating characteristics are far from nominal properties.

A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

PubMed

Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming

2015-01-01

Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

DEVELOPMENT AND EVALUATION OF AN INTEGRATED MODEL TO FACILITATE RISK-BASED CORRECTIVE ACTION AT SUPERFUND SITES

EPA Science Inventory

We developed a numerical model to predict chemical concentrations in indoor environments resulting from soil vapor intrusion and volatilization from groundwater. The model, which integrates new and existing algorithms for chemical fate and transport, was originally...

High Throughput Exposure Prioritization of Chemicals Using a Screening-Level Probabilistic SHEDS-Lite Exposure Model

EPA Science Inventory

These novel modeling approaches for screening, evaluating and classifying chemicals based on the potential for biologically-relevant human exposures will inform toxicity testing and prioritization for chemical risk assessment. The new modeling approach is derived from the Stocha...

Comparison of National Operative Mortality in Gastroenterological Surgery Using Web-based Prospective Data Entry Systems.

PubMed

Anazawa, Takayuki; Paruch, Jennifer L; Miyata, Hiroaki; Gotoh, Mitsukazu; Ko, Clifford Y; Cohen, Mark E; Hirahara, Norimichi; Zhou, Lynn; Konno, Hiroyuki; Wakabayashi, Go; Sugihara, Kenichi; Mori, Masaki

2015-12-01

International collaboration is important in healthcare quality evaluation; however, few international comparisons of general surgery outcomes have been accomplished. Furthermore, predictive model application for risk stratification has not been internationally evaluated. The National Clinical Database (NCD) in Japan was developed in collaboration with the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), with a goal of creating a standardized surgery database for quality improvement. The study aimed to compare the consistency and impact of risk factors of 3 major gastroenterological surgical procedures in Japan and the United States (US) using web-based prospective data entry systems: right hemicolectomy (RH), low anterior resection (LAR), and pancreaticoduodenectomy (PD).Data from NCD and ACS-NSQIP, collected over 2 years, were examined. Logistic regression models were used for predicting 30-day mortality for both countries. Models were exchanged and evaluated to determine whether the models built for one population were accurate for the other population.We obtained data for 113,980 patients; 50,501 (Japan: 34,638; US: 15,863), 42,770 (Japan: 35,445; US: 7325), and 20,709 (Japan: 15,527; US: 5182) underwent RH, LAR, and, PD, respectively. Thirty-day mortality rates for RH were 0.76% (Japan) and 1.88% (US); rates for LAR were 0.43% versus 1.08%; and rates for PD were 1.35% versus 2.57%. Patient background, comorbidities, and practice style were different between Japan and the US. In the models, the odds ratio for each variable was similar between NCD and ACS-NSQIP. Local risk models could predict mortality using local data, but could not accurately predict mortality using data from other countries.We demonstrated the feasibility and efficacy of the international collaborative research between Japan and the US, but found that local risk models remain essential for quality improvement.

Empirical assessment of debris flow risk on a regional scale in Yunnan province, southwestern China.

PubMed

Liu, Xilin; Yue, Zhong Qi; Tham, Lesliw George; Lee, Chack Fan

2002-08-01

Adopting the definition suggested by the United Nations, a risk model for regional debris flow assessment is presented. Risk is defined as the product of hazard and vulnerability, both of which are necessary for evaluation. A Multiple-Factor Composite Assessment Model is developed for quantifying regional debris flow hazard by taking into account eight variables that contribute to debris flow magnitude and its frequency of occurrence. Vulnerability is a measure of the potential total losses. On a regional scale, it can be measured by the fixed asset, gross domestic product, land resources, population density, as well as the age, education, and wealth of the inhabitants. A nonlinear power-function assessment model that accounts for these indexes is developed. As a case study, the model is applied to compute the hazard, vulnerability and risk for each prefecture of the Yunnan province in southwestern China.

A measurement model of perinatal stressors: identifying risk for postnatal emotional distress in mothers of high-risk infants.

PubMed

DeMier, R L; Hynan, M T; Hatfield, R F; Varner, M W; Harris, H B; Manniello, R L

2000-01-01

A measurement model of perinatal stressors was first evaluated for reliability and then used to identify risk factors for postnatal emotional distress in high-risk mothers. In Study 1, six measures (gestational age of the baby, birthweight, length of the baby's hospitalization, a postnatal complications rating for the infant, and Apgar scores at 1 and 5 min) were obtained from chart reviews of preterm births at two different hospitals. Confirmatory factor analyses revealed that the six measures could be accounted for by three factors: (a) Infant Maturity, (b) Apgar Ratings, and (c) Complications. In Study 2, a modified measurement model indicated that Infant Maturity and Complications were significant predictors of postnatal emotional distress in an additional sample of mothers. This measurement model may also be useful in predicting (a) other measures of psychological distress in parents, and (b) measures of cognitive and motor development in infants.

Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry.

PubMed

Wen, Wanqing; Shu, Xiao-Ou; Guo, Xingyi; Cai, Qiuyin; Long, Jirong; Bolla, Manjeet K; Michailidou, Kyriaki; Dennis, Joe; Wang, Qin; Gao, Yu-Tang; Zheng, Ying; Dunning, Alison M; García-Closas, Montserrat; Brennan, Paul; Chen, Shou-Tung; Choi, Ji-Yeob; Hartman, Mikael; Ito, Hidemi; Lophatananon, Artitaya; Matsuo, Keitaro; Miao, Hui; Muir, Kenneth; Sangrajrang, Suleeporn; Shen, Chen-Yang; Teo, Soo H; Tseng, Chiu-Chen; Wu, Anna H; Yip, Cheng Har; Simard, Jacques; Pharoah, Paul D P; Hall, Per; Kang, Daehee; Xiang, Yongbing; Easton, Douglas F; Zheng, Wei

2016-12-08

Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry. We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk. We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P < 0.05. Compared with women in the middle quintile of the PRS, women in the top 1% group had a 2.70-fold elevated risk of breast cancer (95% CI: 2.15-3.40). The risk prediction model with the PRS had an area under the receiver operating characteristic curve of 0.606. The lifetime risk of breast cancer for Shanghai Chinese women in the lowest and highest 1% of the PRS was 1.35% and 10.06%, respectively. Approximately one-half of GWAS-identified breast cancer risk variants can be directly replicated in East Asian women. Collectively, common genetic variants are important predictors for breast cancer risk. Using common genetic variants for breast cancer could help identify women at high risk of breast cancer.

A microRNA-based prediction model for lymph node metastasis in hepatocellular carcinoma.

PubMed

Zhang, Li; Xiang, Zuo-Lin; Zeng, Zhao-Chong; Fan, Jia; Tang, Zhao-You; Zhao, Xiao-Mei

2016-01-19

We developed an efficient microRNA (miRNA) model that could predict the risk of lymph node metastasis (LNM) in hepatocellular carcinoma (HCC). We first evaluated a training cohort of 192 HCC patients after hepatectomy and found five LNM associated predictive factors: vascular invasion, Barcelona Clinic Liver Cancer stage, miR-145, miR-31, and miR-92a. The five statistically independent factors were used to develop a predictive model. The predictive value of the miRNA-based model was confirmed in a validation cohort of 209 consecutive HCC patients. The prediction model was scored for LNM risk from 0 to 8. The cutoff value 4 was used to distinguish high-risk and low-risk groups. The model sensitivity and specificity was 69.6 and 80.2%, respectively, during 5 years in the validation cohort. And the area under the curve (AUC) for the miRNA-based prognostic model was 0.860. The 5-year positive and negative predictive values of the model in the validation cohort were 30.3 and 95.5%, respectively. Cox regression analysis revealed that the LNM hazard ratio of the high-risk versus low-risk groups was 11.751 (95% CI, 5.110-27.021; P < 0.001) in the validation cohort. In conclusion, the miRNA-based model is reliable and accurate for the early prediction of LNM in patients with HCC.

Interaction of Occupational and Personal Risk Factors in Workforce Health and Safety

PubMed Central

Pandalai, Sudha; Wulsin, Victoria; Chun, HeeKyoung

2012-01-01

Most diseases, injuries, and other health conditions experienced by working people are multifactorial, especially as the workforce ages. Evidence supporting the role of work and personal risk factors in the health of working people is frequently underused in developing interventions. Achieving a longer, healthy working life requires a comprehensive preventive approach. To help develop such an approach, we evaluated the influence of both occupational and personal risk factors on workforce health. We present 32 examples illustrating 4 combinatorial models of occupational hazards and personal risk factors (genetics, age, gender, chronic disease, obesity, smoking, alcohol use, prescription drug use). Models that address occupational and personal risk factors and their interactions can improve our understanding of health hazards and guide research and interventions. PMID:22021293

Applying the Land Use Portfolio Model with Hazus to analyse risk from natural hazard events

USGS Publications Warehouse

Dinitz, Laura B.; Taketa, Richard A.

2013-01-01

This paper describes and demonstrates the integration of two geospatial decision-support systems for natural-hazard risk assessment and management. Hazus is a risk-assessment tool developed by the Federal Emergency Management Agency to identify risks and estimate the severity of risk from natural hazards. The Land Use Portfolio Model (LUPM) is a risk-management tool developed by the U.S. Geological Survey to evaluate plans or actions intended to reduce risk from natural hazards. We analysed three mitigation policies for one earthquake scenario in the San Francisco Bay area to demonstrate the added value of using Hazus and the LUPM together. The demonstration showed that Hazus loss estimates can be input to the LUPM to obtain estimates of losses avoided through mitigation, rates of return on mitigation investment, and measures of uncertainty. Together, they offer a more comprehensive approach to help with decisions for reducing risk from natural hazards.

Risk Assessment in Underground Coalmines Using Fuzzy Logic in the Presence of Uncertainty

NASA Astrophysics Data System (ADS)

Tripathy, Debi Prasad; Ala, Charan Kumar

2018-04-01

Fatal accidents are occurring every year as regular events in Indian coal mining industry. To increase the safety conditions, it has become a prerequisite to performing a risk assessment of various operations in mines. However, due to uncertain accident data, it is hard to conduct a risk assessment in mines. The object of this study is to present a method to assess safety risks in underground coalmines. The assessment of safety risks is based on the fuzzy reasoning approach. Mamdani fuzzy logic model is developed in the fuzzy logic toolbox of MATLAB. A case study is used to demonstrate the applicability of the developed model. The summary of risk evaluation in case study mine indicated that mine fire has the highest risk level among all the hazard factors. This study could help the mine management to prepare safety measures based on the risk rankings obtained.

Development of a Risk-Based Comparison Methodology of Carbon Capture Technologies

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

Engel, David W.; Dalton, Angela C.; Dale, Crystal

2014-06-01

Given the varying degrees of maturity among existing carbon capture (CC) technology alternatives, an understanding of the inherent technical and financial risk and uncertainty associated with these competing technologies is requisite to the success of carbon capture as a viable solution to the greenhouse gas emission challenge. The availability of tools and capabilities to conduct rigorous, risk–based technology comparisons is thus highly desirable for directing valuable resources toward the technology option(s) with a high return on investment, superior carbon capture performance, and minimum risk. To address this research need, we introduce a novel risk-based technology comparison method supported by anmore » integrated multi-domain risk model set to estimate risks related to technological maturity, technical performance, and profitability. Through a comparison between solid sorbent and liquid solvent systems, we illustrate the feasibility of estimating risk and quantifying uncertainty in a single domain (modular analytical capability) as well as across multiple risk dimensions (coupled analytical capability) for comparison. This method brings technological maturity and performance to bear on profitability projections, and carries risk and uncertainty modeling across domains via inter-model sharing of parameters, distributions, and input/output. The integration of the models facilitates multidimensional technology comparisons within a common probabilistic risk analysis framework. This approach and model set can equip potential technology adopters with the necessary computational capabilities to make risk-informed decisions about CC technology investment. The method and modeling effort can also be extended to other industries where robust tools and analytical capabilities are currently lacking for evaluating nascent technologies.« less

Utility of existing diabetes risk prediction tools for young black and white adults: Evidence from the Bogalusa Heart Study.

PubMed

Pollock, Benjamin D; Hu, Tian; Chen, Wei; Harville, Emily W; Li, Shengxu; Webber, Larry S; Fonseca, Vivian; Bazzano, Lydia A

2017-01-01

To evaluate several adult diabetes risk calculation tools for predicting the development of incident diabetes and pre-diabetes in a bi-racial, young adult population. Surveys beginning in young adulthood (baseline age ≥18) and continuing across multiple decades for 2122 participants of the Bogalusa Heart Study were used to test the associations of five well-known adult diabetes risk scores with incident diabetes and pre-diabetes using separate Cox models for each risk score. Racial differences were tested within each model. Predictive utility and discrimination were determined for each risk score using the Net Reclassification Index (NRI) and Harrell's c-statistic. All risk scores were strongly associated (p<.0001) with incident diabetes and pre-diabetes. The Wilson model indicated greater risk of diabetes for blacks versus whites with equivalent risk scores (HR=1.59; 95% CI 1.11-2.28; p=.01). C-statistics for the diabetes risk models ranged from 0.79 to 0.83. Non-event NRIs indicated high specificity (non-event NRIs: 76%-88%), but poor sensitivity (event NRIs: -23% to -3%). Five diabetes risk scores established in middle-aged, racially homogenous adult populations are generally applicable to younger adults with good specificity but poor sensitivity. The addition of race to these models did not result in greater predictive capabilities. A more sensitive risk score to predict diabetes in younger adults is needed. Copyright © 2017 Elsevier Inc. All rights reserved.

Real-Time Optimal Flood Control Decision Making and Risk Propagation Under Multiple Uncertainties

NASA Astrophysics Data System (ADS)

Zhu, Feilin; Zhong, Ping-An; Sun, Yimeng; Yeh, William W.-G.

2017-12-01

Multiple uncertainties exist in the optimal flood control decision-making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood control decision making that constitute the Forecast-Optimization-Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two-step process. We propose a novel SMAA-TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision-making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk-informed decisions with higher reliability.

The contributions of breast density and common genetic variation to breast cancer risk.

PubMed

Vachon, Celine M; Pankratz, V Shane; Scott, Christopher G; Haeberle, Lothar; Ziv, Elad; Jensen, Matthew R; Brandt, Kathleen R; Whaley, Dana H; Olson, Janet E; Heusinger, Katharina; Hack, Carolin C; Jud, Sebastian M; Beckmann, Matthias W; Schulz-Wendtland, Ruediger; Tice, Jeffrey A; Norman, Aaron D; Cunningham, Julie M; Purrington, Kristen S; Easton, Douglas F; Sellers, Thomas A; Kerlikowske, Karla; Fasching, Peter A; Couch, Fergus J

2015-05-01

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

[Notes on the planned German health fund model].

PubMed

Robra, B-P; Mayrhofer, T

2006-10-01

The aim of this paper is to evaluate the planned German health fund model, a special risk adjustment scheme, in terms of competition policy. Starting from the present model of risk adjustment, we have examined the consequences of introducing the fund model on competition in the health insurance market. On the one hand, the risk adjustment fund will, at best, decrease ineffective competition for "good risks". On the other hand, it will increase the pressure of competition inside the health insurance market by providing new incentives to the unemployed to change their sickness fund. Significant economies, however, can only be realised by increasing the competition for contracts between the health insurance companies and the suppliers of medical services. The new risk adjustment fund then will also offer only a limited potential for competition between individual sickness funds. Besides, it remains to be seen to what extent policy-makers are able to achieve an optimally designed risk adjustment fund and whether the sickness funds themselves do not misinterpret the reform as nationalization in disguise and consequently delegate their management responsibilities back to the policy-makers.

QUANTITATIVE PROCEDURES FOR NEUROTOXICOLOGY RISK ASSESSMENT

EPA Science Inventory

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

Predicting Adolescent Perceptions of the Risks and Benefits of Cigarette Smoking: A Longitudinal Investigation

PubMed Central

Morrell, Holly E. R.; Song, Anna V.; Halpern-Felsher, Bonnie L.

2010-01-01

Objective To evaluate developmental changes, personal smoking experiences, and vicarious smoking experiences as predictors of adolescents’ perceptions of the risks and benefits of cigarette smoking over time, in order to identify new and effective targets for youth smoking prevention programs. Design 395 adolescents were surveyed every six months for two school years, from the beginning of 9th grade to the end of 10th grade. Main Outcome Measures Time, participant smoking, friend smoking, parental smoking, and sex were evaluated as predictors of smoking-related short-term risk perceptions, long-term risk perceptions, and benefits perceptions using multilevel modeling techniques. Results Perceptions of benefits did not change over time. Perceptions of risk decreased with time, but not after sex and parental smoking were included in the model. Adolescents with personal smoking experience reported decreasing perceptions of risk and increasing perceptions of benefits over time. Adolescents with more than 6 friends who smoked also reported increasing perceptions of benefits over time. Conclusions Changes in risk perceptions may not purely be the result of developmental processes, but may also be influenced by personal and vicarious experience with smoking. Findings highlight the importance of identifying and targeting modifiable factors that may influence perceptions. PMID:20939640

Proactive assessment of accident risk to improve safety on a system of freeways.

DOT National Transportation Integrated Search

2012-05-01

This report describes the development and evaluation of real-time crash risk-assessment models for four freeway corridors: U.S. Route 101 NB (northbound) and SB (southbound) and Interstate 880 NB and SB. Crash data for these freeway segments for the ...

The Use of a Vehicle Acceleration Exposure Limit Model and a Finite Element Crash Test Dummy Model to Evaluate the Risk of Injuries During Orion Crew Module Landings

NASA Technical Reports Server (NTRS)

Lawrence, Charles; Fasanella, Edwin L.; Tabiei, Ala; Brinkley, James W.; Shemwell, David M.

2008-01-01

A review of astronaut whole body impact tolerance is discussed for land or water landings of the next generation manned space capsule named Orion. LS-DYNA simulations of Orion capsule landings are performed to produce a low, moderate, and high probability of injury. The paper evaluates finite element (FE) seat and occupant simulations for assessing injury risk for the Orion crew and compares these simulations to whole body injury models commonly referred to as the Brinkley criteria. The FE seat and crash dummy models allow for varying the occupant restraint systems, cushion materials, side constraints, flailing of limbs, and detailed seat/occupant interactions to minimize landing injuries to the crew. The FE crash test dummies used in conjunction with the Brinkley criteria provides a useful set of tools for predicting potential crew injuries during vehicle landings.

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.

Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

PubMed

Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

2005-09-01

To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks.

PubMed

Schoop, Rotraut; Beyersmann, Jan; Schumacher, Martin; Binder, Harald

2011-02-01

Prognostic models for time-to-event data play a prominent role in therapy assignment, risk stratification and inter-hospital quality assurance. The assessment of their prognostic value is vital not only for responsible resource allocation, but also for their widespread acceptance. The additional presence of competing risks to the event of interest requires proper handling not only on the model building side, but also during assessment. Research into methods for the evaluation of the prognostic potential of models accounting for competing risks is still needed, as most proposed methods measure either their discrimination or calibration, but do not examine both simultaneously. We adapt the prediction error proposal of Graf et al. (Statistics in Medicine 1999, 18, 2529–2545) and Gerds and Schumacher (Biometrical Journal 2006, 48, 1029–1040) to handle models with competing risks, i.e. more than one possible event type, and introduce a consistent estimator. A simulation study investigating the behaviour of the estimator in small sample size situations and for different levels of censoring together with a real data application follows.

A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method.

PubMed

Papageorgiou, Elpiniki I; Jayashree Subramanian; Karmegam, Akila; Papandrianos, Nikolaos

2015-11-01

Breast cancer is the most deadly disease affecting women and thus it is natural for women aged 40-49 years (who have a family history of breast cancer or other related cancers) to assess their personal risk for developing familial breast cancer (FBC). Besides, as each individual woman possesses different levels of risk of developing breast cancer depending on their family history, genetic predispositions and personal medical history, individualized care setting mechanism needs to be identified so that appropriate risk assessment, counseling, screening, and prevention options can be determined by the health care professionals. The presented work aims at developing a soft computing based medical decision support system using Fuzzy Cognitive Map (FCM) that assists health care professionals in deciding the individualized care setting mechanisms based on the FBC risk level of the given women. The FCM based FBC risk management system uses NHL to learn causal weights from 40 patient records and achieves a 95% diagnostic accuracy. The results obtained from the proposed model are in concurrence with the comprehensive risk evaluation tool based on Tyrer-Cuzick model for 38/40 patient cases (95%). Besides, the proposed model identifies high risk women by calculating higher accuracy of prediction than the standard Gail and NSAPB models. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines as well as previous FCM-based risk prediction methods for BC. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Flood vulnerability evaluation in complex urban areas

NASA Astrophysics Data System (ADS)

Giosa, L.; Pascale, S.; Sdao, F.; Sole, A.; Cantisani, A.

2009-04-01

This paper deals the conception, the development and the subsequent validation of an integrated numerical model for the assessment of systemic vulnerability in complex and urbanized areas, subject to flood risk. The proposed methodology is based on the application of the concept of "systemic vulnerability", the model is a mathematician-decisional model action to estimate the vulnerability of complex a territorial system during a flood event. The model uses a group of "pressure pointers" in order to define, qualitatively and quantitatively, the influence exercised on the territorial system from factors like as those physicists, social, economic, etc.. The model evaluates the exposure to the flood risk of the elements that belong to a system. The proposed model, which is based on the studies of Tamura et al., 2000; Minciardi et al., 2004; Pascale et al., 2008; considers the vulnerability not as a characteristic of a particular element at risk, but as a peculiarity of a complex territorial system, in which the different elements are reciprocally linked in a functional way. The proposed model points out the elements with the major functional lost and that make the whole system critical. This characteristic makes the proposed model able to support a correct territorial planning and a suitable management of the emergency following natural disasters such as floods. The proposed approach was tested on the study area in the city of Potenza, southern Italy.

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.

Quantitative risk assessment of Cryptosporidium in tap water in Ireland.

PubMed

Cummins, E; Kennedy, R; Cormican, M

2010-01-15

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

Tsunami risk zoning in south-central Chile

NASA Astrophysics Data System (ADS)

Lagos, M.

2010-12-01

The recent 2010 Chilean tsunami revealed the need to optimize methodologies for assessing the risk of disaster. In this context, modern techniques and criteria for the evaluation of the tsunami phenomenon were applied in the coastal zone of south-central Chile as a specific methodology for the zoning of tsunami risk. This methodology allows the identification and validation of a scenario of tsunami hazard; the spatialization of factors that have an impact on the risk; and the zoning of the tsunami risk. For the hazard evaluation, different scenarios were modeled by means of numerical simulation techniques, selecting and validating the results that better fit with the observed tsunami data. Hydrodynamic parameters of the inundation as well as physical and socioeconomic vulnerability aspects were considered for the spatialization of the factors that affect the tsunami risk. The tsunami risk zoning was integrated into a Geographic Information System (GIS) by means of multicriteria evaluation (MCE). The results of the tsunami risk zoning show that the local characteristics and their location, together with the concentration of poverty levels, establish spatial differentiated risk levels. This information builds the basis for future applied studies in land use planning that tend to minimize the risk levels associated to the tsunami hazard. This research is supported by Fondecyt 11090210.

Patient-specific analysis of blood stasis in the left atrium

NASA Astrophysics Data System (ADS)

Flores, Oscar; Gonzalo, Alejandro; Garcia-Villalba, Manuel; Rossini, Lorenzo; Hsiao, Albert; McVeigh, Elliot; Kahn, Andrew M.; Del Alamo, Juan C.

2016-11-01

Atrial fibrillation (AF) is a common arrhythmia in which the left atrium (LA) beats rapidly and irregularly. Patients with AF are at increased risk of thromboembolic events (TE), particularly stroke. Anticoagulant therapy can reduce the risk of TE in AF, but it can also increase the risks of adverse events such as internal bleeding. The current lack of tools to predict each patient's risk of LA thrombogenesis makes it difficult to decide whether to anticoagulate patients with AF. The aim of this work is to evaluate blood stasis in patient-specific models of the LA, because stasis is a known thrombogenesis risk factor. To achieve our aim, we performed direct numerical simulations of left atrial flow using an immersed boundary solver developed at the UC3M, coupled to a 0D model for the pulmonary circulation. The LA geometry is obtained from time-resolved CT scans and the parameters of the 0D model are found by fitting pulmonary vein flow data obtained by 4D phase contrast MRI. Blood stasis is evaluated from the flow data by computing blood residence time together with other kinematic indices of the velocity field (e.g. strain and kinetic energy). We focus on the flow in the left atrial appendage, including a sensitivity analysis of the effect of the parameters of the 0D model. Funded by the Spanish MECD, the Clinical and Translational Research Institute at UCSD and the American Heart Association.

Gene-environment interaction between adiponectin gene polymorphisms and environmental factors on the risk of diabetic retinopathy.

PubMed

Li, Yuan; Wu, Qun Hong; Jiao, Ming Li; Fan, Xiao Hong; Hu, Quan; Hao, Yan Hua; Liu, Ruo Hong; Zhang, Wei; Cui, Yu; Han, Li Yuan

2015-01-01

To evaluate whether the adiponectin gene is associated with diabetic retinopathy (DR) risk and interaction with environmental factors modifies the DR risk, and to investigate the relationship between serum adiponectin levels and DR. Four adiponectin polymorphisms were evaluated in 372 DR cases and 145 controls. Differences in environmental factors between cases and controls were evaluated by unconditional logistic regression analysis. The model-free multifactor dimensionality reduction method and traditional multiple regression models were applied to explore interactions between the polymorphisms and environmental factors. Using the Bonferroni method, we found no significant associations between four adiponectin polymorphisms and DR susceptibility. Multivariate logistic regression found that physical activity played a protective role in the progress of DR, whereas family history of diabetes (odds ratio 1.75) and insulin therapy (odds ratio 1.78) were associated with an increased risk for DR. The interaction between the C-11377 G (rs266729) polymorphism and insulin therapy might be associated with DR risk. Family history of diabetes combined with insulin therapy also increased the risk of DR. No adiponectin gene polymorphisms influenced the serum adiponectin levels. Serum adiponectin levels did not differ between the DR group and non-DR group. No significant association was identified between four adiponectin polymorphisms and DR susceptibility after stringent Bonferroni correction. The interaction between C-11377G (rs266729) polymorphism and insulin therapy, as well as the interaction between family history of diabetes and insulin therapy, might be associated with DR susceptibility.

An Interactive Risk Detection Tool to Aid Decision-Making in Global Mangrove Restoration

NASA Astrophysics Data System (ADS)

Goldberg, L.; Lagomasino, D.

2017-12-01

Mangrove ecosystems hold high ecological and economic value in coastal communities worldwide; detecting potential regions of mangrove stress is therefore critical to strategic planning of forest and coastal resources. In order to address the need for a unified risk management system for mangrove loss, a Risk Evaluation for MAngroves Portal (REMaP) was developed to identify the locations and causes of mangrove degradation worldwide, as well as project future areas of stress or loss. Long-term Earth observations from LANDSAT, MODIS, and TRMM were used in identifying regions with low, medium, and high vulnerability. Regions were categorized by vulnerability level based upon disturbance metrics in NDVI, land surface temperature, and precipitation using designated thresholds. Natural risks such as erosion and degradation were also evaluated through an analysis of NDVI time series trends from calendar year 1984 to 2017. Future trends in ecosystem vulnerability and resiliency were modeled using IPCC climate scenarios. Risk maps for anthropogenic-based disturbances such as urbanization and the expansion of agriculture and aquaculture through rice, rubber, shrimp, and oil palm farming were also included. The natural and anthropogenic risk factors evaluated were then aggregated to generate a cumulative estimate for total mangrove vulnerability in each region. This interactive modeling tool can aid decision-making on the regional, national, and international levels on an ongoing basis to continuously identify areas best suited for mangrove restoration measures, assisting governments and local communities in addressing a wide range of Sustainable Development Goals for coastal areas.

Development and validation of a generic finite element vehicle buck model for the analysis of driver rib fractures in real life nearside oblique frontal crashes.

PubMed

Iraeus, Johan; Lindquist, Mats

2016-10-01

Frontal crashes still account for approximately half of all fatalities in passenger cars, despite several decades of crash-related research. For serious injuries in this crash mode, several authors have listed the thorax as the most important. Computer simulation provides an effective tool to study crashes and evaluate injury mechanisms, and using stochastic input data, whole populations of crashes can be studied. The aim of this study was to develop a generic buck model and to validate this model on a population of real-life frontal crashes in terms of the risk of rib fracture. The study was conducted in four phases. In the first phase, real-life validation data were derived by analyzing NASS/CDS data to find the relationship between injury risk and crash parameters. In addition, available statistical distributions for the parameters were collected. In the second phase, a generic parameterized finite element (FE) model of a vehicle interior was developed based on laser scans from the A2MAC1 database. In the third phase, model parameters that could not be found in the literature were estimated using reverse engineering based on NCAP tests. Finally, in the fourth phase, the stochastic FE model was used to simulate a population of real-life crashes, and the result was compared to the validation data from phase one. The stochastic FE simulation model overestimates the risk of rib fracture, more for young occupants and less for senior occupants. However, if the effect of underestimation of rib fractures in the NASS/CDS material is accounted for using statistical simulations, the risk of rib fracture based on the stochastic FE model matches the risk based on the NASS/CDS data for senior occupants. The current version of the stochastic model can be used to evaluate new safety measures using a population of frontal crashes for senior occupants. Copyright © 2016 Elsevier Ltd. All rights reserved.

A simulation-based approach for designing effective field-sampling programs to evaluate contamination risk of groundwater supplies

USGS Publications Warehouse

Nordqvist, R.; Voss, C.I.

1996-01-01

An approach to model discrimination and network design for evaluation of groundwater contamination risk is proposed and demonstrated by application to a site in a glaciofluvial aquifer in Sweden. The approach consists of first hypothesizing alternative conceptual models of hydrogeology at the site on the basis of both quantitative data and qualitative information. The conceptual models are then expressed as two-dimensional numerical models of groundwater flow and solute transport, and model attributes controlling risk to the water supply are determined by simulation. Model predictions of response to a specific field test are made with each model that affects risk. Regions for effective measurement networks are then identified. Effective networks are those that capture sufficient information to determine which of the hypothesized models best describes the system with a minimum of measurement points. For the example site in Sweden, the network is designed such that important system parameters may be accurately estimated at the same time as model discrimination is carried out. The site in Vansbro, Sweden, consists of a water-supply well in an esker separated (by 300m) from a wood preservation and treatment area on the esker flank by only a narrow inlet of a bordering stream. Application of the above-described risk analysis shows that, of all the hydrologic controls and parameters in the groundwater system, the only factor that controls the potential migration of wood-treatment contaminants to the well is whether the inlet's bed is pervious, creating a hydraulic barrier to lateral contaminant transport. Furthermore, the analysis localizes an area near the end of the inlet wherein the most effective measurements of drawdown would be made to discriminate between a permeable and impermeable bed. The location of this optimal area is not obvious prior to application of the above methodology.

Use of fire spread and hydrology models to target forest management on a municipal watershed

Treesearch

Anurag Srivastava; William J. Elliot; Joan Wu

2015-01-01

A small town relies on a forested watershed for its water supply. The forest is at risk for a wildfire. To reduce this risk, some of the watershed will be thinned followed by a prescribed burn. This paper reports on a study to evaluate the impact of such watershed disturbances on water yield. To target management activities, a fire spread model was applied to the...

Modelling Risk to US Military Populations from Stopping Blanket Mandatory Polio Vaccination (Open Access Publisher’s Version)

DTIC Science & Technology

2017-09-14

2014. [24] “United Nations, Department of Economic and Social Affairs, Population Division, World Population Prospects, the 2015 Revision,” http...Research Article Modelling Risk to US Military Populations from Stopping Blanket Mandatory Polio Vaccination Colleen Burgess,1,2 Andrew Burgess,2 and...for polio transmission within military populations interacting with locals in a polio-endemic region to evaluate changes in vaccination policy

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.

Cost-Effectiveness of Staphylococcus aureus Decolonization Strategies in High-Risk Total Joint Arthroplasty Patients.

PubMed

Williams, Devin M; Miller, Andy O; Henry, Michael W; Westrich, Geoffrey H; Ghomrawi, Hassan M K

2017-09-01

The risk of prosthetic joint infection increases with Staphylococcus aureus colonization. The cost-effectiveness of decolonization is controversial. We evaluated cost-effectiveness decolonization protocols in high-risk arthroplasty patients. An analytical model evaluated risk under 3 protocols: 4 swabs, 2 swabs, and nasal swab alone. These were compared to no-screening and universal decolonization strategies. Cost-effectiveness was evaluated from the hospital, patient, and societal perspective. Under base case conditions, universal decolonization and 4-swab strategies were most effective. The 2-swab and universal decolonization strategy were most cost-effective from patient and societal perspectives. From the hospital perspective, universal decolonization was the dominant strategy (much less costly and more effective). S aureus decolonization may be cost-effective for reducing prosthetic joint infections in high-risk patients. These results may have important implications for treatment of patients and for cost containment in a bundled payment system. Copyright © 2017 Elsevier Inc. All rights reserved.

Shear wave elastography predicts hepatocellular carcinoma risk in hepatitis C patients after sustained virological response.

PubMed

Hamada, Koichi; Saitoh, Satoshi; Nishino, Noriyuki; Fukushima, Daizo; Horikawa, Yoshinori; Nishida, Shinya; Honda, Michitaka

2018-01-01

To evaluate the relationship between fibrosis and HCC after sustained virological response (SVR) to treatment for chronic hepatitis C (HCV). This single-center study retrospectively evaluated 196 patients who achieved SVR after HCV infection. The associations of risk factors with HCC development after HCV eradication were evaluated using univariate and multivariate Cox proportional hazards regression models. Among the 196 patients, 8 patients (4.1%) developed HCC after SVR during a median follow-up of 26 months. Multivariate analyses revealed that HCC development was independently associated with age of ≥75 years (risk ratio [RR] = 35.16), α- fetoprotein levels of ≥6 ng/mL (RR = 40.30), and SWE results of ≥11 kPa (RR = 28.71). Our findings indicate that SWE may facilitate HCC surveillance after SVR and the identification of patients who have an increased risk of HCC after HCV clearance.

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.

Accuracy, calibration and clinical performance of the EuroSCORE: can we reduce the number of variables?

PubMed

Ranucci, Marco; Castelvecchio, Serenella; Menicanti, Lorenzo; Frigiola, Alessandro; Pelissero, Gabriele

2010-03-01

The European system for cardiac operative risk evaluation (EuroSCORE) is currently used in many institutions and is considered a reference tool in many countries. We hypothesised that too many variables were included in the EuroSCORE using limited patient series. We tested different models using a limited number of variables. A total of 11150 adult patients undergoing cardiac operations at our institution (2001-2007) were retrospectively analysed. The 17 risk factors composing the EuroSCORE were separately analysed and ranked for accuracy of prediction of hospital mortality. Seventeen models were created by progressively including one factor at a time. The models were compared for accuracy with a receiver operating characteristics (ROC) analysis and area under the curve (AUC) evaluation. Calibration was tested with Hosmer-Lemeshow statistics. Clinical performance was assessed by comparing the predicted with the observed mortality rates. The best accuracy (AUC 0.76) was obtained using a model including only age, left ventricular ejection fraction, serum creatinine, emergency operation and non-isolated coronary operation. The EuroSCORE AUC (0.75) was not significantly different. Calibration and clinical performance were better in the five-factor model than in the EuroSCORE. Only in high-risk patients were 12 factors needed to achieve a good performance. Including many factors in multivariable logistic models increases the risk for overfitting, multicollinearity and human error. A five-factor model offers the same level of accuracy but demonstrated better calibration and clinical performance. Models with a limited number of factors may work better than complex models when applied to a limited number of patients. Copyright (c) 2009 European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved.

Integration of second cancer risk calculations in a radiotherapy treatment planning system

NASA Astrophysics Data System (ADS)

Hartmann, M.; Schneider, U.

2014-03-01

Second cancer risk in patients, in particular in children, who were treated with radiotherapy is an important side effect. It should be minimized by selecting an appropriate treatment plan for the patient. The objectives of this study were to integrate a risk model for radiation induced cancer into a treatment planning system which allows to judge different treatment plans with regard to second cancer induction and to quantify the potential reduction in predicted risk. A model for radiation induced cancer including fractionation effects which is valid for doses in the radiotherapy range was integrated into a treatment planning system. From the three-dimensional (3D) dose distribution the 3D-risk equivalent dose (RED) was calculated on an organ specific basis. In addition to RED further risk coefficients like OED (organ equivalent dose), EAR (excess absolute risk) and LAR (lifetime attributable risk) are computed. A risk model for radiation induced cancer was successfully integrated in a treatment planning system. Several risk coefficients can be viewed and used to obtain critical situations were a plan can be optimised. Risk-volume-histograms and organ specific risks were calculated for different treatment plans and were used in combination with NTCP estimates for plan evaluation. It is concluded that the integration of second cancer risk estimates in a commercial treatment planning system is feasible. It can be used in addition to NTCP modelling for optimising treatment plans which result in the lowest possible second cancer risk for a patient.

A site-specific farm-scale GIS approach for reducing groundwater contamination by pesticides

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

Mulla, D.J.; Perillo, C.A.; Cogger, C.G.

1996-05-01

It has been proposed to vary pesticide applications by patterns in surface organic C to reduce the potential for contamination of groundwater. To evaluate the feasibility of this {open_quotes}precision farming{close_quotes} approach, data for carbofuran concentrations from 57 locations sampled to a depth of 180 cm were fit to the convective-dispersive equation. Fitted values for pore water velocity (v) ranged from 0.17 to 1.92 cm d{sup -1}, with a mean of 0.68 cm d{sup -1}. Values for dispersion (D) ranged from 0.29 to 13.35 cm{sup 2} d{sup -1}, with a mean of 2.57. With this data, risks of pesticide leaching weremore » estimated at each location using the attenuation factor (AF) model, and a dispersion based leached factor (LF) model. Using the AF model gave two locations with a very high pesticide leaching risk, 6 with a low risk, and 2 with no risk. Using the LF model, 6 had a high risk, 13 had a medium risk, 18 had a low risk, and 20 had no risk. Pesticide leaching risks were not correlated with any measured surface soil properties. Much of the variability in leaching risk was because of velocity variations, so it would be incorrect to assume that surface organic C content controls the leaching risk. 30 refs., 1 fig., 3 tabs.« less

Clearing margin system in the futures markets—Applying the value-at-risk model to Taiwanese data

NASA Astrophysics Data System (ADS)

Chiu, Chien-Liang; Chiang, Shu-Mei; Hung, Jui-Cheng; Chen, Yu-Lung

2006-07-01

This article sets out to investigate if the TAIFEX has adequate clearing margin adjustment system via unconditional coverage, conditional coverage test and mean relative scaled bias to assess the performance of three value-at-risk (VaR) models (i.e., the TAIFEX, RiskMetrics and GARCH-t). For the same model, original and absolute returns are compared to explore which can accurately capture the true risk. For the same return, daily and tiered adjustment methods are examined to evaluate which corresponds to risk best. The results indicate that the clearing margin adjustment of the TAIFEX cannot reflect true risks. The adjustment rules, including the use of absolute return and tiered adjustment of the clearing margin, have distorted VaR-based margin requirements. Besides, the results suggest that the TAIFEX should use original return to compute VaR and daily adjustment system to set clearing margin. This approach would improve the funds operation efficiency and the liquidity of the futures markets.

Stress and anger as contextual factors and preexisting cognitive schemas: predicting parental child maltreatment risk.

PubMed

Rodriguez, Christina M; Richardson, Michael J

2007-11-01

Progress in the child maltreatment field depends on refinements in leading models. This study examines aspects of social information processing theory (Milner, 2000) in predicting physical maltreatment risk in a community sample. Consistent with this theory, selected preexisting schema (external locus-of-control orientation, inappropriate developmental expectations, low empathic perspective-taking ability, and low perceived attachment relationship to child) were expected to predict child abuse risk beyond contextual factors (parenting stress and anger expression). Based on 115 parents' self-report, results from this study support cognitive factors that predict abuse risk (with locus of control, perceived attachment, or empathy predicting different abuse risk measures, but not developmental expectations), although the broad contextual factors involving negative affectivity and stress were consistent predictors across abuse risk markers. Findings are discussed with regard to implications for future model evaluations, with indications the model may apply to other forms of maltreatment, such as psychological maltreatment or neglect.

Trade Studies of Space Launch Architectures using Modular Probabilistic Risk Analysis

NASA Technical Reports Server (NTRS)

Mathias, Donovan L.; Go, Susie

2006-01-01

A top-down risk assessment in the early phases of space exploration architecture development can provide understanding and intuition of the potential risks associated with new designs and technologies. In this approach, risk analysts draw from their past experience and the heritage of similar existing systems as a source for reliability data. This top-down approach captures the complex interactions of the risk driving parts of the integrated system without requiring detailed knowledge of the parts themselves, which is often unavailable in the early design stages. Traditional probabilistic risk analysis (PRA) technologies, however, suffer several drawbacks that limit their timely application to complex technology development programs. The most restrictive of these is a dependence on static planning scenarios, expressed through fault and event trees. Fault trees incorporating comprehensive mission scenarios are routinely constructed for complex space systems, and several commercial software products are available for evaluating fault statistics. These static representations cannot capture the dynamic behavior of system failures without substantial modification of the initial tree. Consequently, the development of dynamic models using fault tree analysis has been an active area of research in recent years. This paper discusses the implementation and demonstration of dynamic, modular scenario modeling for integration of subsystem fault evaluation modules using the Space Architecture Failure Evaluation (SAFE) tool. SAFE is a C++ code that was originally developed to support NASA s Space Launch Initiative. It provides a flexible framework for system architecture definition and trade studies. SAFE supports extensible modeling of dynamic, time-dependent risk drivers of the system and functions at the level of fidelity for which design and failure data exists. The approach is scalable, allowing inclusion of additional information as detailed data becomes available. The tool performs a Monte Carlo analysis to provide statistical estimates. Example results of an architecture system reliability study are summarized for an exploration system concept using heritage data from liquid-fueled expendable Saturn V/Apollo launch vehicles.

Economic evaluation of using a genetic test to direct breast cancer chemoprevention in white women with a previous breast biopsy.

PubMed

Green, Linda E; Dinh, Tuan A; Hinds, David A; Walser, Bryan L; Allman, Richard

2014-04-01

Tamoxifen therapy reduces the risk of breast cancer but increases the risk of serious adverse events including endometrial cancer and thromboembolic events. The cost effectiveness of using a commercially available breast cancer risk assessment test (BREVAGen™) to inform the decision of which women should undergo chemoprevention by tamoxifen was modeled in a simulated population of women who had undergone biopsies but had no diagnosis of cancer. A continuous time, discrete event, mathematical model was used to simulate a population of white women aged 40-69 years, who were at elevated risk for breast cancer because of a history of benign breast biopsy. Women were assessed for clinical risk of breast cancer using the Gail model and for genetic risk using a panel of seven common single nucleotide polymorphisms. We evaluated the cost effectiveness of using genetic risk together with clinical risk, instead of clinical risk alone, to determine eligibility for 5 years of tamoxifen therapy. In addition to breast cancer, the simulation included health states of endometrial cancer, pulmonary embolism, deep-vein thrombosis, stroke, and cataract. Estimates of costs in 2012 US dollars were based on Medicare reimbursement rates reported in the literature and utilities for modeled health states were calculated as an average of utilities reported in the literature. A 50-year time horizon was used to observe lifetime effects including survival benefits. For those women at intermediate risk of developing breast cancer (1.2-1.66 % 5-year risk), the incremental cost-effectiveness ratio for the combined genetic and clinical risk assessment strategy over the clinical risk assessment-only strategy was US$47,000, US$44,000, and US$65,000 per quality-adjusted life-year gained, for women aged 40-49, 50-59, and 60-69 years, respectively (assuming a price of US$945 for genetic testing). Results were sensitive to assumptions about patient adherence, utility of life while taking tamoxifen, and cost of genetic testing. From the US payer's perspective, the combined genetic and clinical risk assessment strategy may be a moderately cost-effective alternative to using clinical risk alone to guide chemoprevention recommendations for women at intermediate risk of developing breast cancer.

Comparison of different risk stratification systems in predicting short-term serious outcome of syncope patients.

PubMed

Safari, Saeed; Baratloo, Alireza; Hashemi, Behrooz; Rahmati, Farhad; Forouzanfar, Mohammad Mehdi; Motamedi, Maryam; Mirmohseni, Ladan

2016-01-01

Determining etiologic causes and prognosis can significantly improve management of syncope patients. The present study aimed to compare the values of San Francisco, Osservatorio Epidemiologico sulla Sincope nel Lazio (OESIL), Boston, and Risk Stratification of Syncope in the Emergency Department (ROSE) score clinical decision rules in predicting the short-term serious outcome of syncope patients. The present diagnostic accuracy study with 1-week follow-up was designed to evaluate the predictive values of the four mentioned clinical decision rules. Screening performance characteristics of each model in predicting mortality, myocardial infarction (MI), and cerebrovascular accidents (CVAs) were calculated and compared. To evaluate the value of each aforementioned model in predicting the outcome, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated and receiver-operating curve (ROC) curve analysis was done. A total of 187 patients (mean age: 64.2 ± 17.2 years) were enrolled in the study. Mortality, MI, and CVA were seen in 19 (10.2%), 12 (6.4%), and 36 (19.2%) patients, respectively. Area under the ROC curve for OESIL, San Francisco, Boston, and ROSE models in prediction the risk of 1-week mortality, MI, and CVA was in the 30-70% range, with no significant difference among models ( P > 0.05). The pooled model did not show higher accuracy in prediction of mortality, MI, and CVA compared to others ( P > 0.05). This study revealed the weakness of all four evaluated models in predicting short-term serious outcome of syncope patients referred to the emergency department without any significant advantage for one among others.

Preconception healthcare delivery at a population level: construction of public health models of preconception care.

PubMed

Shannon, Geordan D; Alberg, Corinna; Nacul, Luis; Pashayan, Nora

2014-08-01

A key challenge of preconception healthcare is identifying how it can best be delivered at a population level. To review current strategies of preconception healthcare, explore methods of preconception healthcare delivery, and develop public health models which reflect different preconception healthcare pathways. Preconception care strategies, programmes and evaluations were identified through a review of Medline and Embase databases. Search terms included: preconception, pre-pregnancy, intervention, primary care, healthcare, model, delivery, program, prevention, trial, effectiveness, congenital disorders OR abnormalities, evaluation, assessment, impact. Inclusion criteria for review articles were: (1) English, (2) human subjects, (3) women of childbearing age, (4) 1980–current data, (5) all countries, (6) both high risk and universal approaches, (7) guidelines or recommendations, (8) opinion articles, (9) experimental studies. Exclusion criteria were: (1) non-human subjects, (2) non-English, (3) outside of the specified timeframe, (4) articles on male healthcare. The results of the literature review were synthesised into public health models of care: (1) primary care; (2) hospital-based and inter-conception care; (3) specific preconception care clinics; and, (4) community outreach. Fifteen evaluations of preconception care were identified. Community programmes demonstrated a significant impact on substance use, folic acid supplementation, diabetes optimization, and hyperphenylalaninemia. An ideal preconception visits entail risk screening, education, and intervention if indicated. Subsequently, four public health models were developed synthesizing preconception care delivery at a population level. Heterogeneity of risk factors, health systems and strategies of care reflect the lack of consensus about the best way to deliver preconception care. The proposed models aim to reflect differing aspects of preconception healthcare delivery.

Application and comparison of the FADES, MADIT, and SHFM-D risk models for risk stratification of prophylactic implantable cardioverter-defibrillator treatment

PubMed Central

van der Heijden, Aafke C.; van Rees, Johannes B.; Levy, Wayne C.; van der Bom, Johanna G.; Cannegieter, Suzanne C.; de Bie, Mihàly K.; van Erven, Lieselot; Schalij, Martin J.; Borleffs, C. Jan  Willem

2017-01-01

Aims Implantable cardioverter-defibrillator (ICD) treatment is beneficial in selected patients. However, it remains difficult to accurately predict which patients benefit most from ICD implantation. For this purpose, different risk models have been developed. The aim was to validate and compare the FADES, MADIT, and SHFM-D models. Methods and results All patients receiving a prophylactic ICD at the Leiden University Medical Center were evaluated. Individual model performance was evaluated by C-statistics. Model performances were compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI). The primary endpoint was non-benefit of ICD treatment, defined as mortality without prior ventricular arrhythmias requiring ICD intervention. A total of 1969 patients were included (age 63 ± 11 years; 79% male). During a median follow-up of 4.5 ± 3.9 years, 318 (16%) patients died without prior ICD intervention. All three risk models were predictive for event-free mortality (all: P < 0.001). The C-statistics were 0.66, 0.69, and 0.75, respectively, for FADES, MADIT, and SHFM-D (all: P < 0.001). Application of the SHFM-D resulted in an improved IDI of 4% and NRI of 26% compared with MADIT; IDI improved 11% with the use of SHFM-D instead of FADES (all: P < 0.001), but NRI remained unchanged (P = 0.71). Patients in the highest-risk category of the MADIT and SHFM-D models had 1.7 times higher risk to experience ICD non-benefit than receive appropriate ICD interventions [MADIT: mean difference (MD) 20% (95% CI: 7–33%), P = 0.001; SHFM-D: MD 16% (95% CI: 5–27%), P = 0.005]. Patients in the highest-risk category of FADES were as likely to experience ICD intervention as ICD non-benefit [MD 3% (95% CI: –8 to 14%), P = 0.60]. Conclusion The predictive and discriminatory value of SHFM-D to predict non-benefit of ICD treatment is superior to FADES and MADIT in patients receiving prophylactic ICD treatment. PMID:28130376

Quantifying cardiometabolic risk using modifiable non-self-reported risk factors.

PubMed

Marino, Miguel; Li, Yi; Pencina, Michael J; D'Agostino, Ralph B; Berkman, Lisa F; Buxton, Orfeu M

2014-08-01

Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance. To develop and validate a cumulative general cardiometabolic risk score that focuses on non-self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, which may not individually cross clinical cut-off points for risk categories. We prospectively followed 2,359 cardiovascular disease (CVD)-free subjects from the Framingham offspring cohort over a 14-year follow-up. Baseline (fifth offspring examination cycle) included HbA1c and cholesterol measurements. Gender-specific Cox proportional hazards models were considered to evaluate the effects of non-self-reported modifiable risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, BMI, and HbA1c) on general CVD risk. We constructed 10-year general cardiometabolic risk score functions and evaluated its predictive performance in 2012-2013. HbA1c was significantly related to general CVD risk. The proposed cardiometabolic general CVD risk model showed good predictive performance as determined by cross-validated discrimination (male C-index=0.703, 95% CI=0.668, 0.734; female C-index=0.762, 95% CI=0.726, 0.801) and calibration (lack-of-fit chi-square=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively). This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk on the basis of modifiable risk factors that can motivate an individual's commitment to prevention and intervention. Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Quantifying Cardiometabolic Risk Using Modifiable Non–Self-Reported Risk Factors

PubMed Central

Marino, Miguel; Li, Yi; Pencina, Michael J.; D’Agostino, Ralph B.; Berkman, Lisa F.; Buxton, Orfeu M.

2014-01-01

Background Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance. Purpose To develop and validate a cumulative general cardiometabolic risk score that focuses on non–self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, which may not individually cross clinical cut off points for risk categories. Methods We prospectively followed 2,359 cardiovascular disease (CVD)-free subjects from the Framingham offspring cohort over a 14–year follow-up. Baseline (fifth offspring examination cycle) included HbA1c and cholesterol measurements. Gender–specific Cox proportional hazards models were considered to evaluate the effects of non–self-reported modifiable risk factors (blood pressure, total cholesterol, high–density lipoprotein cholesterol, smoking, BMI, and HbA1c) on general CVD risk. We constructed 10–year general cardiometabolic risk score functions and evaluated its predictive performance in 2012–2013. Results HbA1c was significantly related to general CVD risk. The proposed cardiometabolic general CVD risk model showed good predictive performance as determined by cross-validated discrimination (male C-index=0.703, 95% CI=0.668, 0.734; female C-index=0.762, 95% CI=0.726, 0.801) and calibration (lack-of-fit χ2=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively). Conclusions This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk based on modifiable risk factors that can motivate an individual’s commitment to prevention and intervention. PMID:24951039

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

PubMed Central

Tammariello, Alexandra E.; Milner, John A.

2010-01-01

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

Improving the evidence base for services working with youth at-risk of involvement in the criminal justice system: developing a standardised program approach.

PubMed

Knight, Alice; Maple, Myfanwy; Shakeshaft, Anthony; Shakehsaft, Bernie; Pearce, Tania

2018-04-16

Young people who engage in multiple risk behaviour (high-risk young people) such as substance abuse, antisocial behaviour, low engagement in education and employment, self-harm or suicide ideation are more likely to experience serious harms later in life including homelessness, incarceration, violence and premature death. In addition to personal disadvantage, these harms represent an avoidable social and economic cost to society. Despite these harms, there is insufficient evidence about how to improve outcomes for high-risk young people. A key reason for this is a lack of standardisation in the way in which programs provided by services are defined and evaluated. This paper describes the development of a standardised intervention model for high-risk young people. The model can be used by service providers to achieve greater standardisation across their programs, outcomes and outcome measures. To demonstrate its feasibility, the model is applied to an existing program for high-risk young people. The development and uptake of a standardised intervention model for these programs will help to more rapidly develop a larger and more rigorous evidence-base to improve outcomes for high-risk young people.

A Case Study Application of the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) Frameworks to Facilitate the Integration of Human Health and Ecological End Points for Cumulative Risk Assessment (CRA)

EPA Science Inventory

Cumulative risk assessment (CRA) methods promote the use of a conceptual site model (CSM) to apportion exposures and integrate risk from multiple stressors. While CSMs may encompass multiple species, evaluating end points across taxa can be challenging due to data availability an...

Review of methods for developing regional probabilistic risk assessments, part 2: modeling invasive plant, insect, and pathogen species

Treesearch

P. B. Woodbury; D. A. Weinstein

2010-01-01

We reviewed probabilistic regional risk assessment methodologies to identify the methods that are currently in use and are capable of estimating threats to ecosystems from fire and fuels, invasive species, and their interactions with stressors. In a companion chapter, we highlight methods useful for evaluating risks from fire. In this chapter, we highlight methods...