Risk Analysis: An International Journal

Consumption of Fish and Shrimp from Southeast Louisiana Poses No Unacceptable Lifetime Cancer Risks Attributable to High-Priority Polycyclic Aromatic Hydrocarbons

13 March 2018 - 9:40pm

Following oil spills such as the Deepwater Horizon accident (DWH), contamination of seafood resources and possible increased health risks attributable to consumption of seafood in spill areas are major concerns. In this study, locally harvested finfish and shrimp were collected from research participants in southeast Louisiana and analyzed for polycyclic aromatic hydrocarbons (PAHs). PAHs are some of the most important chemicals of concern regarding oil-spill-contaminated seafood resources during and following oil spills. Some PAHs are considered carcinogens for risk assessment purposes, and currently, seven of these can be combined in lifetime cancer risk assessments using EPA approaches. Most PAHs were not detected in these samples (minimum detection limits ranged from 1.2 to 2.1 PPB) and of those that were detected, they were generally below 10 PPB. The pattern of detected PAHs suggested that the source of these chemicals in these seafood samples was not a result of direct contact with crude oil. Lifetime cancer risks were assessed using conservative assumptions and models in a probabilistic framework for the seven carcinogenic PAHs. Lifetime health risks modeled using this framework did not exceed a 1/10,000 cancer risk threshold. Conservative, health-protective deterministic estimates of the levels of concern for PAH chemical concentration and seafood intake rates were above the concentrations and intake rates modeled under this probabilistic framework. Taken together, consumption of finfish and shrimp harvested from southeast Louisiana following the DWH does not pose unacceptable lifetime cancer risks from these seven carcinogenic PAHs even for the heaviest possible consumers.

Mitigating Sports Injury Risks Using Internet of Things and Analytics Approaches

12 March 2018 - 4:06pm

Sport injuries restrict participation, impose a substantial economic burden, and can have persisting adverse effects on health-related quality of life. The effective use of Internet of Things (IoT), when combined with analytics approaches, can improve player safety through identification of injury risk factors that can be addressed by targeted risk reduction training activities. Use of IoT devices can facilitate highly efficient quantification of relevant functional capabilities prior to sport participation, which could substantially advance the prevailing sport injury management paradigm. This study introduces a framework for using sensor-derived IoT data to supplement other data for objective estimation of each individual college football player's level of injury risk, which is an approach to injury prevention that has not been previously reported. A cohort of 45 NCAA Division I-FCS college players provided data in the form of self-ratings of persisting effects of previous injuries and single-leg postural stability test. Instantaneous change in body mass acceleration (jerk) during the test was quantified by a smartphone accelerometer, with data wirelessly transmitted to a secure cloud server. Injuries sustained from the beginning of practice sessions until the end of the 13-game season were documented, along with the number of games played by each athlete over the course of a 13-game season. Results demonstrate a strong prediction model. Our approach may have strong relevance to the estimation of injury risk for other physically demanding activities. Clearly, there is great potential for improvement of injury prevention initiatives through identification of individual athletes who possess elevated injury risk and targeted interventions.

A Framework to Understand Extreme Space Weather Event Probability

12 March 2018 - 4:05pm

An extreme space weather event has the potential to disrupt or damage infrastructure systems and technologies that many societies rely on for economic and social well-being. Space weather events occur regularly, but extreme events are less frequent, with a small number of historical examples over the last 160 years. During the past decade, published works have (1) examined the physical characteristics of the extreme historical events and (2) discussed the probability or return rate of select extreme geomagnetic disturbances, including the 1859 Carrington event. Here we present initial findings on a unified framework approach to visualize space weather event probability, using a Bayesian model average, in the context of historical extreme events. We present disturbance storm time (Dst) probability (a proxy for geomagnetic disturbance intensity) across multiple return periods and discuss parameters of interest to policymakers and planners in the context of past extreme space weather events. We discuss the current state of these analyses, their utility to policymakers and planners, the current limitations when compared to other hazards, and several gaps that need to be filled to enhance space weather risk assessments.

Issue Information - TOC

8 March 2018 - 8:22pm

From the Editors

8 March 2018 - 8:22pm

Quantitative Uncertainty Analysis for a Weed Risk Assessment System

6 March 2018 - 9:35pm

Weed risk assessments (WRA) are used to identify plant invaders before introduction. Unfortunately, very few incorporate uncertainty ratings or evaluate the effects of uncertainty, a fundamental risk component. We developed a probabilistic model to quantitatively evaluate the effects of uncertainty on the outcomes of a question-based WRA tool for the United States. In our tool, the uncertainty of each response is rated as Negligible, Low, Moderate, or High. We developed the model by specifying the likelihood of a response changing for each uncertainty rating. The simulations determine if responses change, select new responses, and sum the scores to determine the risk rating. The simulated scores reveal potential variation in WRA risk ratings. In testing with 204 species assessments, the ranges of simulated risk scores increased with greater uncertainty, and analyses for most species produced simulated risk ratings that differed from the baseline WRA rating. Still, the most frequent simulated rating matched the baseline rating for every High Risk species, and for 87% of all tested species. The remaining 13% primarily involved ambiguous Low Risk results. Changing final ratings based on the uncertainty analysis results was not justified here because accuracy (match between WRA tool and known risk rating) did not improve. Detailed analyses of three species assessments indicate that assessment uncertainty may be best reduced by obtaining evidence for unanswered questions, rather than obtaining additional evidence for questions with responses. This analysis represents an advance in interpreting WRA results, and has enhanced our regulation and management of potential weed species.