Research on risk is built on a complex array of diverse and sometimes inconsistent definitions, constructs, models, and outcomes. This study examines various literatures to formulate an integrated ...framework for the conceptualization of perceived-risk processing. The framework specifies three phases (framing, assessment, and evaluation) and their accompanying outcomes of risk attention, perceived risk, and risk-taking propensity. Explicit linkages are specified between situational and individual characteristics. Perceived-risk evaluation is identified as conceptually distinct from assessment of perceived risk, and the construct of risk-taking propensity is separated from those of risk affinity and perceived risk. The framework further presents points of intersection between the literatures on perceived risk and the literatures on consumer decision-making, information search, and satisfaction. Finally, it serves as an anchor for framing future research to promote conceptual and methodological consistency, and to guide progress in directions that are consistent with some leading edge paradigms outside of marketing. PUBLICATION ABSTRACT
Samples for sediment and two species of native mangrove plants were collected from seven sampling sites for assessing the level of metal contamination. Results of the studied metals displayed the ...order of pollution as Fe > Ti > Zr > Rb > Zn > Sr > Pb > Y > Cu > Cr > As accordingly. Geoaccumulation index and contamination factor revealed that the sediment samples were unpolluted to moderately polluted by Zn, Fe, Ti, Rb, Y, and Zr. Ecological risk factor depicted a pollution-free condition in the study areas. PCA, CA, and correlation coefficient indicated that the source of the metals in the environment was anthropogenic. Bioconcentration factor values were found to be below 1 in both plant species. Conversely, transfer factor values for most heavy metals were found to be >1 in both plant species, which reflects the phytoremediation ability of plants.
•Sediment and mangrove plants were sampled to assess contamination levels of heavy metals in the ship breaking area.•Igeo and CF revealed that the sediment samples were unpolluted to moderately polluted by Zn, Fe, Ti, Rb, Y, and Zr.•CA, PCA and correlation matrix indicates that the source of the metals in the environment was anthropogenic.•TF values for maximum heavy metals were found > 1 in both plant species reflecting the phytoremediation ability of plants.
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it ...requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.
Public trust in the authorities has been recognised in risk research as a crucial component of effective and efficient risk management. But in a pandemic, where the primary responsibility of risk ...management is not centralised within institutional actors but defused across society, trust can become a double-edged sword. Under these conditions, public trust based on a perception of government competence, care and openness may in fact lead people to underestimate risks and thus reduce their belief in the need to take individual action to control the risks. In this paper, we examine the interaction between trust in government, risk perceptions and public compliance in Singapore in the period between January and April 2020. Using social media tracking and online focus group discussions, we present a preliminary assessment of public responses to government risk communication and risk management measures. We highlight the unique deployment of risk communication in Singapore based on the narrative of 'defensive pessimism' to heighten rather than lower levels perceived risk. But the persistence of low public risk perceptions and concomitant low levels of compliance with government risk management measures bring to light the paradox of trust. This calls for further reflection on another dimension of trust which focuses on the role of the public; and further investigation into other social and cultural factors that may have stronger influence over individual belief in the need to take personal actions to control the risks.
Psychiatric disorders present distinct clinical challenges which are partly attributable to their multifactorial aetiology and the absence of laboratory tests that can be used to confirm diagnosis or ...predict risk. Psychiatric disorders are highly heritable, but also polygenic, with genetic risk conferred by interactions between thousands of variants of small effect that can be summarized in a polygenic risk score. We discuss four areas in which the use of polygenic risk scores in psychiatric research and clinical contexts could have ethical implications. First, there is concern that clinical use of polygenic risk scores may exacerbate existing health inequities. Second, research findings regarding polygenic risk could be misinterpreted in stigmatising or discriminatory ways. Third, there are concerns associated with testing minors as well as eugenics concerns elicited by prenatal polygenic risk testing. Fourth, potential challenges that could arise with the feedback and interpretation of high polygenic risk for a psychiatric disorder would require consideration. While there would be extensive overlap with the challenges of feeding back genetic findings in general, the potential clinical use of polygenic risk scoring warrants discussion in its own right, given the recency of this possibility. To this end, we discuss how lay interpretations of risk and genetic information could intersect. Consideration of these factors would be necessary for ensuring effective and constructive communication and interpretation of polygenic risk information which, in turn, could have implications for the uptake of any therapeutic recommendations. Recent advances in polygenic risk scoring have major implications for its clinical potential, however, care should be taken to ensure that communication of polygenic risk does not feed into problematic assumptions regarding mental disorders or support reductive interpretations.
This study was performed to highlight the relationship between single dietary risk factors and cardiovascular diseases (CVDs) in the WHO European Region. We used the comparative risk assessment ...framework of the Global Burden of Disease Study to estimate CVD mortality attributable to diet; comprising eleven forms of CVDs, twelve food and nutrient groups and 27 risk-outcome pairs in four GBD regions including 51 countries by age and sex between 1990 and 2016. In 2016, dietary risks were associated with 2.1 million cardiovascular deaths (95% uncertainty interval (UI), 1.7–2.5 million) in the WHO European Region, accounting for 22.4% of all deaths and 49.2% of CVD deaths. In terms of single dietary risks, a diet low in whole grains accounted for approximately 429,000 deaths, followed by a diet low in nuts and seeds (341,000 deaths), a diet low in fruits (262,000 deaths), a diet high in sodium (251,000 deaths), and a diet low in omega-3 fatty acids (227,000 deaths). Thus, with an optimized, i.e. balanced diet, roughly one in every five premature deaths could be prevented. Although agestandardized death rates decreased over the last 26 years, the absolute number of diet-related cardiovascular deaths increased between 2010 and 2016 by 25,600 deaths in Western Europe and by 4300 deaths in Central Asia. In 2016, approximately 601,000 deaths (28.6% of all diet-related CVD deaths) occurred among adults younger than 70 years. Compared to other behavioural risk factors, a balanced diet is a potential key lever to avoid premature deaths.
The sharing economy is of increasing importance for various aspects of our society by providing people with cost-efficient and convenient access to services or products. However, the profitability ...and healthy development of the sharing economy platforms are greatly restricted by the intensive uncertainties arising from product quality. How to determine the optimal product quality improvement efforts and the optimal prices for the sharing platforms becomes crucial. Moreover, it is reasonable that many sharing economy platforms and consumers are holding a risk-averse attitude during decision making in the highly volatile market. In this paper, we employ the Mean-Variance theory to model the risk-averse attitudes of decision makers, and analytically derive the optimal average quality levels and prices that the platform should provide for the market. We also explore how the critical factors like product quality uncertainty and risk sensitivity affect the platform's equilibrium decisions and consumer surplus. Our analytical results reveal the importance of reducing the difficulty of improving product quality for enhancing the platforms' profitability and consumer surplus. Besides, we identify that it is not always wise for platforms to invest in improving product quality, especially when the related cost becomes increasingly expensive. Moreover, it is shown that the increasing risk aversion or product quality volatility will induce the platform to reduce his quality improvement efforts. Finally, we interestingly find that the platform will always suffer if he becomes risk averse from risk neutral, while consumers may benefit from this attitude change.
Urban soil pollution with heavy metals is one of the environmental problems in recent years, especially in industrial cities. The aim of this study is to evaluate the role of geogenic and ...anthropogenic sources in the urban soil pollution in Yazd, Iran. For this purpose, 30 top-soil (0-10 cm) samples from Yazd within an area of 136.37 Km2 and population of nearly 656 thousand are collected, and the concentration of heavy elements is measured. To evaluate factors affecting the concentration of heavy elements in urban soils and determine their possible sources, Multivariate statistical analysis, including correlation coefficient, principal components analysis (PCA) and cluster analysis (CA) are performed. Enrichment Factor (EF), Geo-accumulation index (Igeo), and Modified potential ecological Risk Index (MRI) are used to assess the level and extension of contamination. Results of this study suggest that As, Cd, Pb and Zn are affected by anthropogenic source, while the concentrations of Fe, Mn, Ni, Cr, Co, Cu and Cs have come from mostly natural geologic sources. As, Cd and Pb are considerably enriched in the area, provided moderately enriched for the elements Mn, Zn and Cu. However, the other heavy elements show minimal enrichment. Igeo reveal that Co, Cr, Cs, Cu, Fe, Mn, Zn and Ni with negative values are unpolluted, Pb posed unpolluted to moderately polluted, and As and Cd represent high polluted. Based on the results of the ecological risk factor, the heavy metals of Mn, Ni, Cr, Zn and Cu have a low ecological risk level. More specifically, we find that Pb shows a moderated ecological risk in 39% of the urban soil in the studied area. As and Cd with respectively 100 and 72% contribution have considerable and very high ecological risk. According to the results of MRI, the area is in a very high ecological risk level, and appropriate management practice is essential to reduce the pollution of heavy elements in this area.
Crises like the COVID-19 pandemic are wake-up calls for enterprises to review their current risk management models. This book suggests a more robust risk management maturity model and illustrates the ...application in crisis situations. The book surveys existing risk management maturity models and proposes a new model appropriate for assessing the risk management processes in enterprises during times of crisis. Its key advantages include the correlation of its attributes with crisis situations and an innovative methodological approach to model development. The authors use the model to examine 107 enterprises from the financial services, construction and IT sector, showing how it allows the user to identify risk management maturity changes in the aftermath of the COVID-19 pandemic. The book will interest entrepreneurs, managers and risk management professionals, who can use the model in their management processes, as well as enterprise stakeholders and academics.
To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).
Using data from 8,756 patients free at ...baseline of HF, with <10% missing data, and enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident HF. The RSF model was externally validated in a cohort of individuals with T2DM using the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).
Over a median follow-up of 4.9 years, 319 patients (3.6%) developed incident HF. The RSF models demonstrated better discrimination than the best performing Cox-based method (C-index 0.77 95% CI 0.75-0.80 vs. 0.73 0.70-0.76 respectively) and had acceptable calibration (Hosmer-Lemeshow statistic χ
= 9.63,
= 0.29) in the internal validation data set. From the identified predictors, an integer-based risk score for 5-year HF incidence was created: the WATCH-DM (Weight BMI, Age, hyperTension, Creatinine, HDL-C, Diabetes control fasting plasma glucose, QRS Duration, MI, and CABG) risk score. Each 1-unit increment in the risk score was associated with a 24% higher relative risk of HF within 5 years. The cumulative 5-year incidence of HF increased in a graded fashion from 1.1% in quintile 1 (WATCH-DM score ≤7) to 17.4% in quintile 5 (WATCH-DM score ≥14). In the external validation cohort, the RSF-based risk prediction model and the WATCH-DM risk score performed well with good discrimination (C-index = 0.74 and 0.70, respectively), acceptable calibration (
≥0.20 for both), and broad risk stratification (5-year HF risk range from 2.5 to 18.7% across quintiles 1-5).
We developed and validated a novel, machine learning-derived risk score that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among outpatients with T2DM.
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