Risk Assessment Overrides: Shuffling the Risk Deck Without Any Improvements in Prediction

2020 ◽  
Vol 47 (12) ◽  
pp. 1609-1629
Author(s):  
Thomas H. Cohen ◽  
Christopher T. Lowenkamp ◽  
Kristin Bechtel ◽  
Anthony W. Flores
Keyword(s):  
Risk Assessment ◽  
Risk Prediction ◽  
Risk Classification ◽  
Federal System ◽  
Sex Offenses ◽  
Supervision System ◽  
Risk Levels ◽  
Risk Reclassification ◽  
The Impact

In the federal supervision system, officers have discretion to depart from the risk designations provided by the Post Conviction Risk Assessment (PCRA) instrument. This component of the risk classification process is referred to as the supervision override. While the rationale for allowing overrides is that actuarial scores cannot always capture an individual’s unique characteristics, there is relatively limited literature on the actual effects of overrides on an actuarial tool’s predictive efficacies. This study examines overrides in the federal system by assessing the extent to which risk levels are adjusted through overrides as well as the impact of overrides on the PCRA’s risk prediction effectiveness. Findings show that nearly all overrides lead to an upward risk reclassification, that overrides tend to place substantial numbers of persons under federal supervision (especially those convicted of sex offenses) into the highest supervision categories, and that overrides result in a deterioration of the PCRA’s risk prediction capacities.

Applying the risk principle to optimize accuracy and equity in correctional risk assessment: Results From a Simulation

2021 ◽  
pp. 0306624X2098652
Author(s):  
Grant Duwe
Keyword(s):  
Risk Assessment ◽  
Predictive Accuracy ◽  
Ethnic Disparities ◽  
Simulated Data ◽  
Risk Assessments ◽  
Risk Level ◽  
Risk Levels ◽  
Risk Principle ◽  
Changes In Risk ◽  
The Impact

As the use of risk assessments for correctional populations has grown, so has concern that these instruments exacerbate existing racial and ethnic disparities. While much of the attention arising from this concern has focused on how algorithms are designed, relatively little consideration has been given to how risk assessments are used. To this end, the present study tests whether application of the risk principle would help preserve predictive accuracy while, at the same time, mitigate disparities. Using a sample of 9,529 inmates released from Minnesota prisons who had been assessed multiple times during their confinement on a fully-automated risk assessment, this study relies on both actual and simulated data to examine the impact of program assignment decisions on changes in risk level from intake to release. The findings showed that while the risk principle was used in practice to some extent, the simulated results showed that greater adherence to the risk principle would increase reductions in risk levels and minimize the disparities observed at intake. The simulated data further revealed the most favorable outcomes would be achieved by not only applying the risk principle, but also by expanding program capacity for the higher-risk inmates in order to adequately reduce their risk.

scholarly journals A Review on Automatic Mammographic Density and Parenchymal Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-31 ◽  
Author(s):  
Wenda He ◽  
Arne Juette ◽  
Erika R. E. Denton ◽  
Arnau Oliver ◽  
Robert Martí ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Assessment ◽  
Risk Prediction ◽  
Prediction Models ◽  
Prevention Measures ◽  
Clinical Environment ◽  
Tissue Segmentation ◽  
Risk Prediction Models ◽  
The Impact

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

Abstract 14505: Risk Reclassification by Circulating Immune Markers in Ambulatory Us Adults Without Prevalent Cardiovascular Diseases: Insights From National Health and Nutrition Examination Survey

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Kartik Gupta ◽  
Rajat Kalra ◽  
Mike Pate ◽  
Shivaraj Nagalli ◽  
Sameer Ather ◽  
...  
Keyword(s):  
Risk Score ◽  
Predictive Value ◽  
Logistic Models ◽  
Risk Classification ◽  
Total Leukocyte Count ◽  
Time To Event ◽  
Risk Reclassification ◽  
Ascvd Risk ◽  
Cv Disease ◽  
The Impact

Introduction: Inflammation is associated with worse cardiovascular (CV) prognosis. We evaluated the impact of adding elevated monocyte lymphocyte ratio (MLR) to 10-year atherosclerotic CV disease (ASCVD) risk score. Hypothesis: Adding elevated MLR improves risk classification by 10-year ASCVD risk score. Methods: We used data from 6 cycles of NHANES (1999-2010) to identify ambulatory US adults aged ≥ 18 years without prevalent cardiovascular disease and total leukocyte count in normal range (4,000-11,000 cells/μL). Elevated MLR (≥0.3) was defined using optimal cutoff to predict CV mortality by Youden index. CV mortality was derived from linked National Center for Health Statistics data. We compared the change in reclassification of the 10-year ASCVD risk score at a pre-specified cutoff of 5% (low risk) and reported the net reclassification index (NRI). We further compared predictive value of the circulating immune with risk factors in ASCVD risk score using time-to-event and logistic models. Results: Among 21,599 eligible participants with median age 47 years (IQR, 34, 63; 49.2% women and 49.5% non-Hispanic White), there were 627 CV deaths with annual incidence rate of 0.3% over median follow-up of 9.6 years (IQR 6.8, 13.1). Median ASCVD risk score was 5% (IQR 1.6, 16.7). Adding elevated MLR to categorical 10-year ASCVD risk score correctly up classified 3.2% participants with CV mortality and incorrectly up classified 0.4% participants with no CV mortality. There was significant improvement in risk classification (NRI 2.7±1.4%, p=0.044, Panel A). Adding elevated CRP (≥0.3 mg/dL) did not change risk class (NRI -0.2±0.0%, p=827, Panel B). Among ASCVD components, MLR had higher predictive value than race, smoking, high-density lipoprotein, and total cholesterol in both time-to-event and logistic models. Conclusions: Elevated MLR significantly improves CV risk reclassification in ambulatory adults without prevalent CV disease and is better than CRP.

Professional Discretion and the Predictive Validity of a Juvenile Risk Assessment Instrument

2016 ◽  
Vol 15 (2) ◽  
pp. 103-118 ◽  
Author(s):  
James T. McCafferty
Keyword(s):  
Risk Assessment ◽  
Predictive Validity ◽  
Assessment Tool ◽  
Assessment Instrument ◽  
Adjusted Risk ◽  
Risk Levels ◽  
Risk Assessment Instrument ◽  
Actuarial Risk ◽  
Using Data ◽  
The Impact

The ability for professionals to override the results of an actuarial risk assessment tool is an essential part of effective correctional risk classification; however, little is known about how this important function affects the predictive validity of these tools. Using data from a statewide sample of juveniles from Ohio, this study examined the impact of professional adjustments on the predictive validity of a juvenile risk assessment instrument. This study found that the original and adjusted risk levels were significant predictors of recidivism, but the original risk levels were stronger predictors of recidivism than the adjusted risk levels that accounted for overrides.

Application of Gustav in Entertainment Places Fire Risk Assessment

2014 ◽  
Vol 668-669 ◽  
pp. 1413-1416
Author(s):  
Yun Tao Zhao ◽  
Jia Wang
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Fire Risk ◽  
Assessment Method ◽  
Control Measures ◽  
Risk Level ◽  
Risk Levels ◽  
Full Life Cycle ◽  
Fire Risk Assessment ◽  
The Impact

Entertainment places have special structure; large fire load, personnel-intensive features, and function layout often change in the process of operation, so the fire risk level will change frequently. The current fire risk assessment studies for only one stage in entertainment places without considering the impact of risk factors at different stages. For this situation, this paper presents a fire risk assessment method in entertainment places based on full life cycle, divides the entertainment places into different stages, analyzes risk factors at different stages, and then uses the method of Gustav to get the fire risk levels of different stages. The assessment results show that the level of fire risk in entertainment places are different at different stages, you can take the appropriate risk control measures against fire risk factors at different stages, which has important guiding significance for fire risk management in entertainment places.

Strengthening risk prediction using statistical learning in children with autism spectrum disorder

2018 ◽  
Vol 4 (3) ◽  
pp. 141-152 ◽  
Author(s):  
Tanu Tanu ◽  
Deepti Kakkar
Keyword(s):  
Statistical Learning ◽  
Risk Prediction ◽  
Autism Spectrum ◽  
Second Phase ◽  
Prediction Ability ◽  
Content Type ◽  
Risk Levels ◽  
Children With Asd ◽  
The Impact ◽  
Risk Knowledge

Purpose The purpose of this paper is to investigate the prediction ability in children with ASD in the risk-involving situations and compute the impact of statistical learning (SL) in strengthening their risk knowledge. The learning index and stability with time are also calculated by comparing their performance over three consecutive weekly sessions (session 1, session 2 and session 3). Design/methodology/approach Participants were presented with a series of images, showing simple and complex risk-involving situations, using the psychophysical experimental paradigm. The stimuli in the experiment were provided with different levels of difficulty in order to keep the legacy of the prediction and SL-based experiment intact. Findings The first phase of experimental work showed that children with ASD accurately discriminated the risk, although performed poorly as compared to neurotypical. The attenuated response in differentiating risk levels indicates that children with ASD have a poor and underdeveloped sense of risk. The second phase investigated their capability to extract the information from repetitive patterns and calculated SL stability value in time. The learning curve shows that SL is intact and stable with time (average session r=0.74) in children with ASD. Research limitations/implications The present work concludes that impaired action prediction could possibly be one of the factors underlying underdeveloped sense of risk in children with ASD. Their SL capability shows that risk knowledge can be strengthened in them. In future, the studies should investigate the impact of age and individual differences, by using knowledge from repetitive trials, on the learning rate and trajectories. Practical implications SL, being an integral part of different therapies, rehabilitation schemes and intervention systems, has the potential to enhance the cognitive and functional abilities of children with ASD. Originality/value Past studies have provided evidence regarding the work on the prediction ability in individuals with ASD. However, it is unclear whether the risk-involving/dangerous situations play any certain role to enhance the prediction ability in children with ASD. Also, there are limited studies predicting risk knowledge in them. Based on this, the current work has investigated the risk prediction in children with ASD.

Risk assessment of the impact of atmospheric emissions from a preparation plant on population health

2020 ◽  
pp. 184-188
Author(s):  
V. V. Kislitsyna ◽  
Yu. S. Likontseva ◽  
D. V. Surzhikov ◽  
R. A. Golikov
Keyword(s):  
Risk Assessment ◽  
Population Health ◽  
Sio2 Content ◽  
Processing Plant ◽  
Background Concentrations ◽  
Chronic Intoxication ◽  
Risk Levels ◽  
The Impact ◽  
The City ◽  
Risk Assessment Methodology

Introduction. Determining the relationship between the impact of environmental factors and the health status of the population based on the risk assessment methodology is an urgent problem of preventive hygiene. The city of Novokuznetsk in the Kemerovo region, which is a major center of the metallurgical and coal industry, is characterized by a particularly difficult environmental situation.The aim of the study is to assess the risk to population health from air pollution from the emissions of a coal-processing plant.Materials and methods. The work used the volume of maximum permissible emissions of the central processing plant “Abashevskaya”. Calculations of maximum and average annual concentrations of pollutants were performed using the “EcoCenter-Standard” program, based on “Methods for calculating the dispersion of emissions of harmful (polluting) substances in the air”. Population health risks were calculated in accordance with the “Guidelines for assessing public health risks from exposure to chemicals that pollute the environment”. The resulting risk values were compared with acceptable values. Also, the values of risk levels were determined considering background concentrations.Results. Priority pollutants were identified: nitrogen dioxide, nitrogen oxide, carbon monoxide, sulfur dioxide, carbon (soot), inorganic dust with a SiO2 content of less than 20%, inorganic dust with a SiO2 content of 20–70%, benzene, manganese and its compounds. The maximum and average concentrations of pollutants were determined and the MPC exceeded at the selected calculation points. It was found that the risk levels of immediate action are zero. The risk levels of chronic intoxication range from 3×10–8 (manganese and its compounds) to 0.003 (inorganic dust with a SiO2 content of less than 20%). The highest total level of risks of chronic intoxication (0.006) is observed in the Baidaevka district. This is due to the location of pollution sources. The highest hazard indexes are also observed in the neighborhood Baidaevka. The hazard coefficients for all substances do not exceed “1”, which indicates that the population is not significantly likely to develop harmful effects with daily intake of the substance during life, and such an impact is acceptable. According to the data obtained, soot and benzene as carcinogenic substances do not pose a danger. The total values of the risks of immediate action, chronic intoxication and carcinogenic risk do not exceed the acceptable level. The total values of the risks of chronic intoxication, taking into account background concentrations, exceed the acceptable level by 2.9–4.1 times.Conclusion. Emissions from the coal-processing plant contribute to air pollution in the city, without significantly affecting the health of the population. The use of the risk assessment methodology is necessary to identify the most unfavorable areas of the city and pollutants that contribute most to the health of the population.The authors declare no conflict of interests.

A Comparison of Inherent Risk Levels in ASME B31.8 and UK Gas Pipeline Design Codes

2006 ◽  
Author(s):  
Graham Goodfellow ◽  
Jane Haswell
Keyword(s):  
Risk Assessment ◽  
Radiation Effects ◽  
Assessment Methods ◽  
Gas Pipeline ◽  
Quantitative Risk Assessment ◽  
Design Codes ◽  
Risk Levels ◽  
Integrity Management ◽  
Pipeline Design ◽  
The Impact

The approach to gas pipeline risk and integrity management in the US, involving the development of integrity management plans for High Consequence Areas (HCA), is usually qualitative, as outlined in ASME B31.8S. Depending on the engineering judgement of the assessment team this can lead to a wide variety of results making risk comparison between pipelines difficult. Qualitative risk ranking methods are popular in Europe, but quantitative risk assessment (QRA) is also used for setting acceptable risk levels and as an input to risk and integrity management planning. It is possible to use quantitative risk assessment methods to compare the levels of risk inherent in different pipeline design codes. This paper discusses the use of pipeline quantitative risk assessment methods to analyse pipelines designed to ASME B31.8 and UK IGE/TD/1 (equivalent to PD 8010, published by BSI, for the design of gas pipelines) codes. The QRA utilises predictive models for consequence assessment, e.g. pipeline blowdown and thermal radiation effects, and failure frequency, in determining the risk levels due to an operational pipeline. The results of the analysis illustrate how the risk levels inherent in the two codes compare for different class locations & minimum housing separation distances. The impact of code requirements on design factor, depth of burial, population density and the impact of third party activity on overall risk levels are also discussed.

The Effects of Corrosion Measurement Error on a Safety Risk Assessment: A TransGas Case Study

2014 ◽  
Author(s):  
Daryl Bandstra ◽  
Corey Gorrill
Keyword(s):  
Risk Assessment ◽  
Risk Measures ◽  
Quantitative Risk Assessment ◽  
Probability Models ◽  
Corrosion Failure ◽  
Safety Risk ◽  
Risk Levels ◽  
Transmission Pipeline ◽  
Operating Risk ◽  
The Impact

The risk of pipeline failure is a measure of the state of knowledge of the pipeline; improved knowledge of the pipeline reduces the uncertainty and therefore can reduce the associated risk. Specifically for corrosion defects, the knowledge of the number and size of defects is often obtained using in-line inspection tools which have uncertainty associated with their measurement capabilities. Quantitative Risk Assessment (QRA) is a methodology that objectively assesses a range of pipeline integrity threats including the threat of corrosion failure. QRA can incorporate the impact of significant sources of analysis uncertainty, such as feature sizing in risk estimates. This paper discusses an application of QRA used to evaluate the operating risk of high pressure transmission pipeline segments in the TransGas system. Specific examples are described in which the inspection tool sizing uncertainty was shown to exert a significant influence on the calculated risk levels. In carrying out the analysis, the failure probability models selected were dependent on the nature of the integrity threat and the type of information available for each pipeline. For the assessment of corrosion integrity, the results of in-line inspections were used directly in determining failure likelihood. For the other threats including equipment impact, geotechnical hazards, manufacturing cracks and stress corrosion cracking, the probability of failure was estimated from historical failure rates with adjustments to reflect line-specific conditions. Failure consequences were estimated using models that quantify the safety implications of loss of containment events. Using these models, safety risk measures were calculated along the length of each pipeline. The results of the analysis show the benefit of the use of inspection technologies with improved sizing accuracy, in terms of reduction in expected operating risk.

Dynamic Risk Assessment of Shoreline Contamination from Ships: Integrating an Oil Spill Model

2014 ◽  
Vol 2014 (1) ◽  
pp. 299678
Author(s):  
Rodrigo Fernandes ◽  
Filipe Lourenço ◽  
Frank Braunschweig ◽  
Ramiro Neves
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Oil Spill ◽  
Numerical Models ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Risk Levels ◽  
Dynamic Risk ◽  
Risk Algorithm ◽  
The Impact

Latest scientific and technological developments on coastal monitoring and operational oceanography have provided the opportunity of building complex and integrative decision support systems for coastal risk management. An innovative methodology to dynamically produce quantified risks has been developed, integrating numerical metocean forecasts and oil spill simulations with the existing monitoring tools. The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in the study area with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the associated oil amount reaching shoreline and the environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behavior model. Shoreline risk is variable in time, based on variable vessel positions (from AIS) and metocean conditions (from operational numerical models). The simultaneous calculation of the risk posed by each vessel crossing a study area is integrated, allowing the generation of a dynamic shoreline risk map for the study area. Shoreline risks can be computed in real time or from previous obtained data. The whole system has been implemented in real time on the Portuguese and Galician Coast. Since several ships cross this area, optimization was performed to allow running the oil spill model for multiple virtual spills from ships along time. The integrated oil spill model uses MOHID lagrangian particle tracking system, where all major transport and weathering processes are considered, including full 3D movement of oil particles, wave-induced currents, and a novel implementation of oil-shoreline interaction. The relevance of integrating the oil spill model in the risk algorithm is evaluated. To perform this, risk levels are compared considering the impact of virtual spilled oil reaching shoreline based on oil spill model simulations, or simply considering the vessel shoreline proximity as impact factor. The integration of an oil spill model in the shoreline risk levels, combined with adequate metocean modeling forecasts, allow a more realistic approach in the assessment of shoreline impacts, which can become even more important in case of regions with greater variability in marine weather conditions. The risk assessment from historic data can help finding typical risk patterns, “hot spots” or developing sensitivity analysis to specific conditions, whereas real time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

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