Uncertainty In Risk Estimates Research Articles

A data quality management framework for equipment failure risk estimation: Application to the oil and gas industry

In engineering asset management, accurate failure risk estimation is essential for averting equipment breakdowns and optimizing risk-based maintenance strategies. Data quality and model fitting are two critical sources of prediction uncertainty in risk estimation. While much attention has been devoted to model fitting for risk estimation, the critical role of data quality has often been overshadowed. To bridge this research gap, this paper presents a novel data quality management framework tailored for industrial equipment failure risk estimation. The framework covers the steps from data to model. It consists of the following main phases: data development, data quality assessment, data quality requirement decision-making, data quality improvement, and risk estimation model development. The framework provides detailed guidelines that can facilitate data practitioners to build individualized data quality requirement decision-making models for failure risk estimation of their equipment. The decision-making model can measure the adequacy of existing data to build a risk estimation model that meets the specified requirements and further determines the best risk estimation model given the available data. A case study using actual data collected during oil well drilling operations from multiple oil fields demonstrates the practicality and effectiveness of the framework. In this case study, four risk estimation models are compared, including two baseline models (mean time to failure and median time to failure) and two machine-learning models (quantile regression and hidden Markov model). In addition, a decision tree-based decision model is developed to determine whether the data quality meets the requirements and the best risk estimation model in case the data quality meets the requirements.

Permethrin Contamination of Sawgrass Marshes and Potential Risk for the Imperiled Klot's Skipper Butterfly (Euphyes pilatka klotsi).

Nontarget effects from mosquito control operations are possible in habitats adjacent to areas targeted by ultra-low-volume (ULV) sprays of permethrin for adult mosquito control. We assessed the risks of permethrin exposure to butterflies, particularly the imperiled Klot's skipper, when exposed to ground-based ULV sprays. Samples of larval host plant leaves (sawgrass) were collected in June (in mosquito season) and January (outside mosquito season) of 2015 from sawgrass marsh habitats of the National Key Deer Wildlife Refuge (Big Pine Key, FL, USA) and analyzed for permethrin. Permethrin detection was higher in June (detected on 70% of samples) than in January (30%), and concentrations were significantly higher in June (geomean = 2.1 ng/g, median = 2.4) relative to January (0.4 ng/g, median = 0.2). Dietary risk for 4th to 5th-instar larvae was low based on the measured residues. The AGricultural DISPersal model (Ver. 8.26) was used to estimate permethrin residues on sawgrass following ULV sprays (deposited residues) to estimate immediate postspray risk. Estimated deposited residues (33-543 ng/g) were much higher than measured residues, which leads to a higher risk likelihood for butterfly larvae immediately after ULV sprays. The difference between estimated and measured residues, and between the two risk estimations, reflects uncertainty in risk estimates based on the measured residues. Research on modeling deposited pesticide residues following ground-based ULV spray is limited. More research on estimating deposited pesticide residues from truck-mounted ULV sprayers could help reduce uncertainty in the risk predictions for nontarget insects like butterflies. Environ Toxicol Chem 2024;43:267-278. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Dynamic risk analysis of evolving scenarios in oil and gas separator

Risk assessment models in the oil and gas (O&G) industry has begun to transform from time-static models like fault tree, event tree or bowtie to dynamic risk assessment (DRA) models, as the latter is able to better capture the real-time-dependent risk behavior of safety barriers. Currently, most existing works on DRA in O&G industry mainly rely on event-driven data, such as failures or accident data from similar systems for risk updating. These data can be collected only when accidents or near-misses have occurred, which limits the prediction capability of the DRA model. To address this drawback, a DRA model is developed in this paper that uses condition-monitoring data for risk updating. A significant advantage of using condition-monitoring data is that the risk can be updated before accidents or failures occur, giving the operation team more time to take preventive actions.The developed approach comprises of an offline and an online phase. In the offline phase, a conventional risk assessment is performed based on fault tree models to calculate the risks at the beginning of the operation. Through the offline analysis, we can also identify the most critical safety barriers by examining their contribution to the risk indexes. Critical safety barriers are selected for condition-monitoring. In the online phase, the condition-monitoring data are used to update the reliability of the safety barriers in real-time based on a sequential Markov Chain Monte Carlo (MCMC) algorithm using a Bayesian framework. The updated reliabilities of the safety barriers are, then, used in the offline risk assessment model for a DRA.The developed approach is applied for a DRA of liquid carryover from an oil and gas separator to downstream equipment, using real-time data from the separator's safety barriers. The results show that the developed method provides a more accurate representation of the system's performance, enabling early detection of potential failures and reducing uncertainty in risk estimates. The proposed DRA model demonstrated its effectiveness in predicting failures of safety barriers in real-time, giving operational teams ample opportunity to take corrective action. This leads to improved decision-making in the O&G industry, enabling timely response to changes in risk levels. The use of condition-monitoring data enhances the accuracy of risk estimations, representing a crucial advance in DRA applications in the O&G sector.

Incidence of and Risk Factors for Active Tuberculosis Disease in Individuals With Glomerular Disease: A Canadian Cohort Study

Kidney failure is an established risk factor for active tuberculosis (TB) but the risk of TB has not been reported in specific kidney diseases. We sought to determine the incidence of and risk factors for active TB in patients with glomerular disease. Observational cohort study. A provincial kidney pathology registry (2000-2012) was used to identify 3,079 adult patients with IgA nephropathy, focal segmental glomerulosclerosis (FSGS), antineutrophil cytoplasmic antibody (ANCA)-related glomerulonephritis, lupus nephritis, membranous nephropathy, minimal change disease, or "other" glomerular diseases in British Columbia, Canada. Predictors included demographics, immigration status, comorbidities, immunosuppression use, estimated glomerular filtration rate (eGFR), and proteinuria. A diagnosis of active TB was ascertained using administrative data linkages and defined based on (1) the dispensation of 1 or more unique combinations of medications used to treat active TB, or (2) physician or hospital visits for active TB. The definition of TB was validated in an external cohort linked to the Provincial TB registry at the BC Centre for Disease Control (BCCDC). Standardized incidence ratios were calculated using the age-matched general population. Risk factors for active TB were identified using Cox proportional hazards regression analysis. The sensitivity and specificity of the outcome definition of active TB were 87.6% and 99.5%, respectively. During a median follow-up of 6.2 years, 41 patients developed active TB with an incidence of 197 of 100,000 person-years, approximately 23 times as high as the general population and>6 times higher than the threshold of 30 per 100,000 used to define high TB incidence. A high incidence was observed in all glomerular diseases (range, 110-403 per 100,000), in both Canadian- and foreign-born patients (range, 124-424 per 100,000), and in patients exposed or not to immunosuppression (282 vs 147 per 100,000). Factors associated with higher TB risk included immigration from a high-incidence country (HR, 3.90 [95% CI, 1.75-8.68]), diminished eGFR (HR, 2.81 [95% CI, 1.18-6.69]), higher levels of proteinuria (HR, 1.15 [95% CI, 1.04-1.27]), lupus nephritis (HR, 2.79 [95% CI, 1.37-5.68]), and immunosuppression use (HR, 2.13 [95% CI, 1.13-4.03]). A relatively low number of events contributed to uncertainty in risk estimates. Patients with glomerular disease have a high incidence of active TB irrespective of disease type, demographics, or use of immunosuppression. Prospective studies are needed to evaluate the utility of screening for latent TB infection in this population. Patients with kidney failure are at high risk of developing tuberculosis (TB), a major infection that can be prevented by identifying and treating patients who have had prior exposure to TB. The risk of TB in specific kidney diseases is unknown. In this Canadian study of 3,079 patients with glomerular disease, a group of autoimmune kidney conditions, the rate of TB was 23 times higher than in the general population. The rate was high irrespective of the use of immunosuppressive drugs or whether patients had immigrated to Canada from another country. These findings suggest that screening patients with glomerular disease for prior TB exposure may be beneficial; however, this needs to be evaluated in a prospective study.

Health risk assessment due to fluoride exposure from groundwater in rural areas of Agra, India: Monte Carlo simulation

The present study deals with the assessment of the fluoride concentration from 60 different spatially distributed well locations and associated health risks to rural residents in the Achhnera block of Agra, Western Uttar Pradesh, India. Monte Carlo simulation and sensitivity analysis were applied to quantify uncertainties in risk estimation. Assessment of the human health risk was carried out using computations based on fluoride ingestion (EDIing), dermal absorption of fluoride (EDIder) and hazard quotient (HQ) in all three categorized groups, i.e., infants, children, and adults. Fluoride concentration analyzed was found to be ranging between 0.15 and 2.50 mg/L with an average value of 1.51 mg/L. More than 58% of samples analyzed surpassed the permissible level as per drinking water specifications. From the model obtained for non-carcinogenic risk due to water ingestion, values of IRw range between 55.4% and 62.5% in all the three exposure groups, while that of Cw ranges between 37% and 42.8%. Based on the HQ investigation it was found that infants were at maximum risk followed by children and the least affected were adults. Results showed that based on the sensitivity analysis, a definition of the probability distributions of CW, IRw, EF and SA is presented as the main factor to improve the accuracy of the result.

Ecological and human health risk assessment of metals leached from end-of-life solar photovoltaics

Photovoltaic industry has shown tremendous growth among renewable energy sector. Though, this high installation rate will eventually result in generation of large volume of end-of-life photovoltaic waste with hazardous metals. In present study, reported leached metal contents from different photovoltaics in previous investigations were utilized for (i) potential fate and transport analysis to soil and groundwater and, (ii) estimating ecological and human health risks via dermal and ingestion pathways for child and adult sub-populations. The results indicate that the children are at highest risk, mainly due to lead (hazard quotient from 1.2 to 2.6). Metals, such as cadmium, lead, indium, molybdenum and tellurium pose maximum risks for child and adult sub-populations via soil-dermal pathway followed by soil-ingestion pathway. This is further proved by calculated high values of contamination factor and geo-accumulation index for cadmium (102.4), indium (238.9) and molybdenum (16.12). The estimated soil contamination is significant with respect to aluminium, silver, cadmium, iron, lead, however, groundwater contamination was insignificant. Exposure to polluted soils yields an aggregate hazard index (for non-cancer effects) > 1 for all four pathways, with soil dermal pathway as the major contributor. Lead poses significant cancer risk for all scenarios (average risk: 0.0098 to 0.047 (soil) and 2.1 × 10−5 to 3.5 × 10−5 (groundwater)), whereas acceptable non-cancer risk was observed for other metals from groundwater exposure. Further, variance contribution and spearman correlation coefficient analysis show that metal concentration, exposure frequency and ingestion rate are the main contributors towards overall uncertainty in risk estimates. More detailed assessment for environmentally-sensitive metals should be carried out by considering other field breakage scenarios also, although the assessment suggests low risk for majority of metals examined.

181P The use of Ki67 testing and scoring in HR+, HER2- early breast cancer

Biomarkers of cellular proliferation (eg. Ki67≥20%) may be useful in estimating recurrence risk in HR+, HER2- early breast cancer (EBC: Stage I-III). The use of chemotherapy (CT) depends on multiple factors including the patient’s (pt) estimated risk of recurrence. One area of uncertainty in risk estimation is in pts with 1-3 positive nodes. This study aimed to understand the use of Ki67 testing and scoring in pts with node+, HR+, HER2- EBC and treatment (trt) patterns.

Modeling DEM Errors in Coastal Flood Inundation and Damages: A Spatial Nonstationary Approach

AbstractDigital elevation model (DEM) as an essential input to flood risk analyzers is subject to a certain level of uncertainty, which increases as the resolution becomes coarser. To quantify this uncertainty, a probabilistic framework incorporating 2‐D hydrodynamic flood mapping by LISFLOOD‐FP along with a sequential Gaussian simulation (SGS) model is presented in this paper. Based on ordinary kriging (OK) interpolation techniques, spatial uncertainty is modeled through SGS by generating several equiprobable realizations. Furthermore, regression kriging (RK) is used in the SGS algorithm utilizing its ability to explore additional information such as terrain characteristics to estimate the elevation error. A new technique called nonstationary sequential Gaussian simulation is also proposed by introducing the spatial nonstationarity of DEM error into SGS framework based on two approaches of uniform and nonuniform moving window. Different DEM resolutions resampled from a 1‐m light detection and ranging‐derived DEM are used in the hydrodynamic model to derive the accuracy‐efficiency trade‐offs and select the most suitable spatial resolution for probabilistic analysis. A detailed comparison among OK, RK, nonstationary OK, and nonstationary RK models is made considering probabilistic floodplain characteristics. The subsequent risk curves are then derived under floods of different return periods. The methodology is implemented in the coastal areas of Lower Manhattan and Brooklyn in New York City. Results show that the proposed methodology could reduce the uncertainty in risk estimation imposed by the stationarity assumption. The methodology could help decision‐makers to more accurately utilize the existing tools and data for flood risk analysis and preparedness.

Implementing a Framework to Evaluate the Impact of Food Intake Shifts on Risk of Illness Using a Case Study with Infant Cereal (P11-118-19)

Abstract Objectives We developed a framework to provide decision makers with a multi-faceted evaluation of the impact of dietary shifts on risk of illness in the U.S. population. Methods We collected representative data on prevalence and concentration of inorganic arsenic and aflatoxin in infant rice and oat cereal. Exposure to these contaminants through consumption and risk of illness from cancer were assessed per consumer based on data from the National Health and Nutrition Examination Survey and published dose-response and related data. The expected number of additional cases of illness and disability-adjusted life years (DALYs) for the U.S. population were estimated. The public health impact of shifts in consumption from one product to the other considered marginal and joint consumption and characterized uncertainty arising from estimates of contaminant concentrations, bioavailability and dose-response models. Monte Carlo simulations were developed in R and a Shiny app was created. Results Based on current consumption of infant rice and oat cereal, the estimated additional DALY for the total US population from inorganic arsenic and aflatoxin is 4,600 (CI 90% [370; 8,400]). If all consumers shift their consumption (maintaining equivalent servings) to only infant rice or only infant oat cereal, the estimated DALY increases to 1.4 and decreases to 0.4 relative to the baseline, respectively. Changes in contaminant concentrations or % consumers also significantly impact risk. Uncertainty in risk estimates is primarily driven by the dose-response models for this case study. The case study showcases applicability of the framework for a wide range of food safety and nutrition questions. Results support previous advice on varying grain intake in children. Conclusions The current risk-risk framework can provide decision makers with a nuanced understanding of the impact of consumption shifts on public health and reveal parameters that drive predicted changes in public health. The Shiny app provides a real-time visualization tool to facilitate understanding and allow direct query by decision makers. Funding Sources This work was carried out under official duties or contract with U.S. FDA.

Doses, Benefits, Safety, and Risks in Oral and Maxillofacial Diagnostic Imaging.

Diagnostic imaging is essential in dentistry. Doses range from low to very low, benefits to patients can be immense, and safe techniques are well known but widely ignored. Doses range from very low with properly executed intraoral, cephalometric, and panoramic imaging to higher than multidetector computed tomography with cone-beam computed tomography. Benefits are substantial: imaged dental disease, often obscured from direct vision by size and anatomy, can pose a mortal threat to the patient. Additionally, imaging is often central in planning complex dental procedures. Safe imaging in dental environments is straightforward; the means for minimizing dose and maximizing diagnostic efficacy have been widely and inexpensively available for decades. Such techniques reduce patient dose by some 80% over traditional techniques but are infrequently used. Digital panoramic equipment reduces doses markedly. For cone-beam computed tomography imaging, selection criteria are critical in defining appropriate fields of view and presets; several publications address this. It is treacherous to discuss risk in oral and maxillofacial radiology. There are more than 330 million dental x-ray examinations annually, the majority being intraoral examinations, with steady increases in panoramic and cone-beam computed tomography. Radiation carcinogenesis from conventional imaging is unlikely, while large field-of-view, high-resolution preset cone-beam computed tomography can be comparable in carcinogenesis risk to craniofacial multidetector computed tomography. Uncertainties in risk estimation from low doses coupled with the huge numbers of dental images taken annually and the rapid growth of cone-beam computed tomography dictate that safe oral and maxillofacial imaging is in the interests of patients, staff, and the public. As low as reasonably achievable (ALARA) practices and linear no-threshold risk modeling continue to be prudent and appropriate.

Probabilistic risk assessment of exposure to fluoride in most consumed brands of tea in the Middle East

The aim of this study was to evaluate the fluoride concentration in seven most consumed brands of tea in the Middle East which was imported to Iran through Zabol County. In the present study, the health risk of exposure to fluoride was estimated using a probabilistic approach. Monte Carlo simulation and sensitivity analysis were applied to quantify uncertainties in risk estimation. The highest mean and 95th percentile of chronic daily intake (CDI) was observed for children group. Iranian tea and Kenya tea had maximum CDI and target hazard quotient (THQ) values among studied brands of tea which followed by Green tea, Taksetare tea, Ceylan tea, Red tea, and White tea. These result indicated that there were significant risks of exposure to fluoride in most of studied brands of tea for children (THQ>1). Sensitivity analysis showed that fluoride concentration and daily intake were the most influential variables in three exposed populations. In conclusion, the fluoride concentration in some studied brands of tea is high and it put children at risk risks of exposure to fluoride.

Application of the Aquifer Impact Model to support decisions at a CO2 sequestration site

AbstractThe National Risk Assessment Partnership (NRAP) has developed a suite of tools to assess and manage risk at CO2 sequestration sites. The NRAP tool suite includes the Aquifer Impact Model (AIM), which evaluates the potential for groundwater impacts from leaks of CO2 and brine through abandoned wellbores. There are two aquifer reduced‐order models (ROMs) included with the AIM tool, a confined alluvium aquifer, and an unconfined carbonate aquifer. The models accept aquifer parameters as a range of variable inputs so they may have broad applicability. The generic aquifer models may be used at the early stages of site selection, when site‐specific data is not available. Guidelines have been developed for determining when the generic ROMs might be applicable to a new site. This paper considers the application of the AIM to predicting the impact of CO2 or brine leakage were it to occur at the Illinois Basin Decatur Project (IBDP). Results of the model sensitivity analysis can help guide characterization efforts; the hydraulic parameters and leakage source term magnitude are more sensitive than clay fraction or cation exchange capacity. Sand permeability was the only hydraulic parameter measured at the IBDP site. More information on the other hydraulic parameters could reduce uncertainty in risk estimates. Some non‐adjustable parameters are significantly different for the ROM than for the observations at the IBDP site. The generic ROMs could be made more useful to a wider range of sites if the initial conditions and no‐impact threshold values were adjustable parameters. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd.

Effects of Acknowledging Uncertainty about Earthquake Risk Estimates on San Francisco Bay Area Residents' Beliefs, Attitudes, and Intentions.

We test here the risk communication proposition that explicit expert acknowledgment of uncertainty in risk estimates can enhance trust and other reactions. We manipulated such a scientific uncertainty message, accompanied by probabilities (20%, 70%, implicit ["will occur"] 100%) and time periods (10 or 30 years) in major (≥magnitude 8) earthquake risk estimates to test potential effects on residents potentially affected by seismic activity on the San Andreas fault in the San Francisco Bay Area (n = 750). The uncertainty acknowledgment increased belief that these specific experts were more honest and open, and led to statistically (but not substantively) significant increases in trust in seismic experts generally only for the 20% probability (vs. certainty) and shorter versus longer time period. The acknowledgment did not change judged risk, preparedness intentions, or mitigation policy support. Probability effects independent of the explicit admission of expert uncertainty were also insignificant except for judged risk, which rose or fell slightly depending upon the measure of judged risk used. Overall, both qualitative expressions of uncertainty and quantitative probabilities had limited effects on public reaction. These results imply that both theoretical arguments for positive effects, and practitioners' potential concerns for negative effects, of uncertainty expression may have been overblown. There may be good reasons to still acknowledge experts' uncertainties, but those merit separate justification and their own empirical tests.

Epidemiological research on radiation-induced cancer in atomic bomb survivors.

The late effects of exposure to atomic bomb radiation on cancer occurrence have been evaluated by epidemiological studies on three cohorts: a cohort of atomic bomb survivors (Life Span Study; LSS), survivors exposed in utero, and children of atomic bomb survivors (F1). The risk of leukemia among the survivors increased remarkably in the early period after the bombings, especially among children. Increased risks of solid cancers have been evident since around 10 years after the bombings and are still present today. The LSS has clarified the dose–response relationships of radiation exposure and risk of various cancers, taking into account important risk modifiers such as sex, age at exposure, and attained age. Confounding by conventional risk factors including lifestyle differences is not considered substantial because people were non-selectively exposed to the atomic bomb radiation. Uncertainty in risk estimates at low-dose levels is thought to be derived from various sources, including different estimates of risk at background levels, uncertainty in dose estimates, residual confounding and interaction, strong risk factors, and exposure to residual radiation and/or medical radiation. The risk of cancer in subjects exposed in utero is similar to that in LSS subjects who were exposed in childhood. Regarding hereditary effects of radiation exposure, no increased risk of cancers associated with parental exposure to radiation have been observed in the F1 cohort to date. In addition to biological and pathogenetic interpretations of the present results, epidemiological investigations using advanced technology should be used to further analyze these cohorts.

An International Union for the Conservation of Nature Red List ecosystems risk assessment for alpine snow patch herbfields, South‐Eastern Australia

AbstractAlpine ecosystems are globally at risk from climate change. We use the International Union for the Conservation of Nature (IUCN) Red List Criteria for ecosystems to assess the risk of ecosystem collapse in Australian alpine snow patch herbfields. These ecosystems occur on both mainland Australia and Tasmania. They are restricted to steep, south‐easterly slopes where snow pack persists well into the summer growing season. Consequently, they are rare, and have high conservation significance. We evaluated the risk of snow patch herbfield ‘ecosystem collapse’ against criteria that accounted for the ecosystem's restricted distribution, projected decline in the snowpack and increased rates of invasion by taller growing native species of shrub and grass. Our analyses revealed considerable uncertainty in estimates of risk based on some criteria, particularly those related to thresholds of ecosystem collapse caused by biotic change. On the basis of the IUCN Red List criteria, we conclude that the ecosystem is ‘endangered’. This is because of the restricted geographical distribution of the ecosystem, a substantial and highly likely decline in the abundance of snow (the principal abiotic driver of the ecosystem), and the prospect of invasion of much of the ecosystem by taller growing native shrubs and grasses. Our case study demonstrates the utility of the Red List methodology for assessing risks to biodiversity in rare ecosystems where changes to both abiotic factors and the relative dominance of native species constitute major threats. Our findings indicate the importance of snow patch herbfields as refugia for dwarf alpine plant species in the face of climate change, the need for continued monitoring, the removal of feral animals from the Australian Alps and scenario planning.

A new approach to reduce uncertainties in space radiation cancer risk predictions.

The prediction of space radiation induced cancer risk carries large uncertainties with two of the largest uncertainties being radiation quality and dose-rate effects. In risk models the ratio of the quality factor (QF) to the dose and dose-rate reduction effectiveness factor (DDREF) parameter is used to scale organ doses for cosmic ray proton and high charge and energy (HZE) particles to a hazard rate for γ-rays derived from human epidemiology data. In previous work, particle track structure concepts were used to formulate a space radiation QF function that is dependent on particle charge number Z, and kinetic energy per atomic mass unit, E. QF uncertainties where represented by subjective probability distribution functions (PDF) for the three QF parameters that described its maximum value and shape parameters for Z and E dependences. Here I report on an analysis of a maximum QF parameter and its uncertainty using mouse tumor induction data. Because experimental data for risks at low doses of γ-rays are highly uncertain which impacts estimates of maximum values of relative biological effectiveness (RBEmax), I developed an alternate QF model, denoted QFγAcute where QFs are defined relative to higher acute γ-ray doses (0.5 to 3 Gy). The alternate model reduces the dependence of risk projections on the DDREF, however a DDREF is still needed for risk estimates for high-energy protons and other primary or secondary sparsely ionizing space radiation components. Risk projections (upper confidence levels (CL)) for space missions show a reduction of about 40% (CL∼50%) using the QFγAcute model compared the QFs based on RBEmax and about 25% (CL∼35%) compared to previous estimates. In addition, I discuss how a possible qualitative difference leading to increased tumor lethality for HZE particles compared to low LET radiation and background tumors remains a large uncertainty in risk estimates.

Integrating basic radiobiological science and epidemiological studies: why and how.

There is quite an extensive set of epidemiology studies conducted for a range of different radiation exposure scenarios and in some cases at doses that can be considered to be in the low dose range (<100 mGy). In addition, there is an extensive literature on the effects of radiation at animal and cellular levels, as well as expanding knowledge of the underlying mechanisms of disease formation (both cancer and non-cancer). A significant concern is that these two areas of study have been linked rarely to support each other-to enhance low dose/low dose-rate extrapolation and reduction of uncertainty in risk estimates. Research should provide specific types of data to support the epidemiology and thereby enhance the radiation risk assessment process. Such an approach is one that can be adapted from that used for chemical exposures. It is based on the concept of adverse outcome pathways for the formation of adverse health outcomes. The adverse outcome pathway conceptual framework is considered to be a logical sequence of events (so-called key events) or processes within biological systems that can be used to understand adverse effects and refine the current risk assessment practice. This approach, which has only been used currently for chemicals, shifts the risk assessment focus from traditional apical endpoints (e.g., cancer and cardiovascular disease) to the development of a mechanistic understanding of effects at a molecular and cellular level. The need is to further the knowledge of the key events in radiation-induced diseases and to provide information on the dose and dose-rate response for these. It is proposed that the key-event approach be used in conjunction with enhanced radiation epidemiology data to reduce overall uncertainty in low dose/low dose-rate cancer and non-cancer risk estimates.

Review of NASA approach to space radiation risk assessments for Mars exploration.

Long duration space missions present unique radiation protection challenges due to the complexity of the space radiation environment, which includes high charge and energy particles and other highly ionizing radiation such as neutrons. Based on a recommendation by the National Council on Radiation Protection and Measurements, a 3% lifetime risk of exposure-induced death for cancer has been used as a basis for risk limitation by the National Aeronautics and Space Administration (NASA) for low-Earth orbit missions. NASA has developed a risk-based approach to radiation exposure limits that accounts for individual factors (age, gender, and smoking history) and assesses the uncertainties in risk estimates. New radiation quality factors with associated probability distribution functions to represent the quality factor's uncertainty have been developed based on track structure models and recent radiobiology data for high charge and energy particles. The current radiation dose limits are reviewed for spaceflight and the various qualitative and quantitative uncertainties that impact the risk of exposure-induced death estimates using the NASA Space Cancer Risk (NSCR) model. NSCR estimates of the number of "safe days" in deep space to be within exposure limits and risk estimates for a Mars exploration mission are described.

Clinical decision-making tools for exam selection, reporting and dose tracking.

Although many efforts have been made to reduce the radiation dose associated with individual medical imaging examinations to "as low as reasonably achievable," efforts to ensure such examinations are performed only when medically indicated and appropriate are equally if not more important. Variations in the use of ionizing radiation for medical imaging are concerning, regardless of whether they occur on a local, regional or national basis. Such variations among practices can be reduced with the use of decision support tools at the time of order entry. These tools help reduce radiation exposure among practices through the appropriate use of medical imaging. Similarly, adoption of best practices among imaging facilities can be promoted through tracking the radiation exposure among imaging patients. Practices can benchmark their aggregate radiation exposures for medical imaging through the use of dose index registries. However several variables must be considered when contemplating individual patient dose tracking. The specific dose measures and the variation among them introduced by variations in body habitus must be understood. Moreover the uncertainties in risk estimation from dose metrics related to age, gender and life expectancy must also be taken into account.

Editorial: steps towards global flood risk modelling

Editorial: steps towards global flood risk modelling