The airEXODUS evacuation model and its application to evacuation certification, crew training and accident investigation.

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in the design and development of safer aircraft, in the implementation of safer and more rigorous certification criteria, cabin crew training and in post mortuum accident investigation. As the risk of personal injury and costs involved in performing large-scale evacuation experiments for the next generation 'Ultra High Capacity Aircraft' (UHCA) are expected to be high, the development and use of these evacuation modelling tools may become essential if these aircraft are to prove a viable reality. In this paper the capabilities and limitations of the airEXODUS evacuation model are described. Its successful application to the prediction of a recent certification trial, prior to the actual trial taking place, is described. Also described is a newly defined parameter known as OPS which can be used as a measure of evacuation trial optimality. In addition, sample evacuation simulations in the presence of fire atmospheres are described. Finally, the data requiremnets of the airEXODUS evacuation model is discussed along with several projects currently underway at the the Univesity of Greenwich designed to obtain this data. Included in this discussion is a description of the AASK - Aircraft Accident Statistics and Knowledge - data base which contains detailed information from aircraft accident survivors.

Chernobyl: Living with Risk and Uncertainty

The nuclear accident in Chernobyl in 1986 is a dramatic example of the type of incidents that are characteristic of a risk society. The consequences of the incident are indeterminate, the causes complex and future developments unpredictable. Nothing can compensate for its effects and it affects a broad population indiscriminately. This paper examines the lived experience of those who experienced biographical disruption as residents of the region on the basis of qualitative case studies carried out in 2003 in the Chernobyl regions of Russia, Ukraine and Belarus. Our analysis indicates that informants tend to view their future as highly uncertain and unpredictable; they experience uncertainty about whether they are already contaminated, and they have to take hazardous decisions about where to go and what to eat. Fear, rumours and experts compete in supplying information to residents about the actual and potential consequences of the disaster, but there is little trust in, and only limited awareness of, the information that is provided. Most informants continue with their lives and do what they must or even what they like, even where the risks are known. They often describe their behaviour as being due to economic circumstances; where there is extreme poverty, even hazardous food sources are better than none. Unlike previous studies, we identify a pronounced tendency among informants not to separate the problems associated with the disaster from the hardships that have resulted from the break-up of the USSR, with both events creating a deep-seated sense of resignation and fatalism. Although most informants hold their governments to blame for lack of information, support and preventive measures, there is little or no collective action to have these put in place. This contrasts with previous research which has suggested that populations affected by disasters attribute crucial significance to that incident and, as a consequence, become increasingly politicized with regard to related policy agendas.

Job strain as a risk factor for coronary heart disease:a collaborative meta-analysis of individual participant data

BACKGROUND: Published work assessing psychosocial stress (job strain) as a risk factor for coronary heart disease is inconsistent and subject to publication bias and reverse causation bias. We analysed the relation between job strain and coronary heart disease with a meta-analysis of published and unpublished studies. METHODS: We used individual records from 13 European cohort studies (1985-2006) of men and women without coronary heart disease who were employed at time of baseline assessment. We measured job strain with questions from validated job-content and demand-control questionnaires. We extracted data in two stages such that acquisition and harmonisation of job strain measure and covariables occurred before linkage to records for coronary heart disease. We defined incident coronary heart disease as the first non-fatal myocardial infarction or coronary death. FINDINGS: 30?214 (15%) of 197?473 participants reported job strain. In 1·49 million person-years at risk (mean follow-up 7·5 years [SD 1·7]), we recorded 2358 events of incident coronary heart disease. After adjustment for sex and age, the hazard ratio for job strain versus no job strain was 1·23 (95% CI 1·10-1·37). This effect estimate was higher in published (1·43, 1·15-1·77) than unpublished (1·16, 1·02-1·32) studies. Hazard ratios were likewise raised in analyses addressing reverse causality by exclusion of events of coronary heart disease that occurred in the first 3 years (1·31, 1·15-1·48) and 5 years (1·30, 1·13-1·50) of follow-up. We noted an association between job strain and coronary heart disease for sex, age groups, socioeconomic strata, and region, and after adjustments for socioeconomic status, and lifestyle and conventional risk factors. The population attributable risk for job strain was 3·4%. INTERPRETATION: Our findings suggest that prevention of workplace stress might decrease disease incidence; however, this strategy would have a much smaller effect than would tackling of standard risk factors, such as smoking. FUNDING: Finnish Work Environment Fund, the Academy of Finland, the Swedish Research Council for Working Life and Social Research, the German Social Accident Insurance, the Danish National Research Centre for the Working Environment, the BUPA Foundation, the Ministry of Social Affairs and Employment, the Medical Research Council, the Wellcome Trust, and the US National Institutes of Health.

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality-forecasting models be associated with real-world trends in health-related variables? Does inclusion of health-related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle-related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models.

Myopia, spectacle wear, and risk of bicycle accidents among rural Chinese secondary school students: the Xichang Pediatric Refractive Error Study report no. 7.

OBJECTIVE: To study the effect of myopia and spectacle wear on bicycle-related injuries in rural Chinese students. Myopia is common among Chinese students but few studies have examined its effect on daily activities. METHODS: Data on visual acuity, refractive error, current spectacle wear, and history of bicycle use and accidents during the past 3 years were sought from 1891 students undergoing eye examinations in rural Guangdong province. RESULTS: Refractive and accident data were available for 1539 participants (81.3%), among whom the mean age was 14.6 years, 52.5% were girls, 26.8% wore glasses, and 12.9% had myopia of less than -4 diopters in both eyes. More than 90% relied on bicycles to get to school daily. A total of 2931 accidents were reported by 423 participants, with 68 requiring medical attention. Male sex (odds ratio, 1.55; P < .001) and spectacle wear (odds ratio, 1.38; P = .04) were associated with a higher risk of accident, but habitual visual acuity and myopia were unassociated with the crash risk, after adjusting for age, sex, time spent riding, and risky riding behaviors. CONCLUSION: These results may be consistent with data on motor vehicle accidents implicating peripheral vision (potentially compromised by spectacle wear) more strongly than central visual acuity in mediating crash risk.

The Baltic Dry Index: cyclicalities, forecasting and hedging strategies

The cyclical properties of the Baltic Dry Index (BDI) and their implications for forecasting performance are investigated. We find that changes in the BDI can lead to permanent shocks to trade of major exporting economies. In our forecasting exercise, we show that commodities and trigonometric regression can lead to improved predictions and then use our forecasting results to perform an investment exercise and to show how they can be used for improved risk management in the freight sector.

Accidental release of hazardous gases: modelling and assessing risk

The renewed concern in assessing risks and consequences from technological hazards in industrial and urban areas continues emphasizing the development of local-scale consequence analysis (CA) modelling tools able to predict shortterm pollution episodes and exposure effects on humans and the environment in case of accident with hazardous gases (hazmat). In this context, the main objective of this thesis is the development and validation of the EFfects of Released Hazardous gAses (EFRHA) model. This modelling tool is designed to simulate the outflow and atmospheric dispersion of heavy and passive hazmat gases in complex and build-up areas, and to estimate the exposure consequences of short-term pollution episodes in accordance to regulatory/safety threshold limits. Five main modules comprising up-to-date methods constitute the model: meteorological, terrain, source term, dispersion, and effects modules. Different initial physical states accident scenarios can be examined. Considered the main core of the developed tool, the dispersion module comprises a shallow layer modelling approach capable to account the main influence of obstacles during the hazmat gas dispersion phenomena. Model validation includes qualitative and quantitative analyses of main outputs by the comparison of modelled results against measurements and/or modelled databases. The preliminary analysis of meteorological and source term modules against modelled outputs from extensively validated models shows the consistent description of ambient conditions and the variation of the hazmat gas release. Dispersion is compared against measurements observations in obstructed and unobstructed areas for different release and dispersion scenarios. From the performance validation exercise, acceptable agreement was obtained, showing the reasonable numerical representation of measured features. In general, quality metrics are within or close to the acceptance limits recommended for ‘non-CFD models’, demonstrating its capability to reasonably predict hazmat gases accidental release and atmospheric dispersion in industrial and urban areas. EFRHA model was also applied to a particular case study, the Estarreja Chemical Complex (ECC), for a set of accidental release scenarios within a CA scope. The results show the magnitude of potential effects on the surrounding populated area and influence of the type of accident and the environment on the main outputs. Overall the present thesis shows that EFRHA model can be used as a straightforward tool to support CA studies in the scope of training and planning, but also, to support decision and emergency response in case of hazmat gases accidental release in industrial and built-up areas.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Risk criteria in occupational environments: critical overview and discussion

The current work can be seen as a starting point for the discussion of the problematic on risk acceptance criteria in occupational environments. Some obstacles to the quantitative acceptance criteria formulation and use were analyzed. A look to the long tradition of major hazards accidents was also performed. This work shows that organizations can have several difficulties in acceptance criteria formulation and that the use of pre-defined acceptance criteria in risk assessment methodologies can be inadequate in some cases. It is urgent to define guidelines that can help organizations in the formulation of risk acceptance criteria for occupational environments.

Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off

Recent work shows that a low correlation between the instruments and the included variables leads to serious inference problems. We extend the local-to-zero analysis of models with weak instruments to models with estimated instruments and regressors and with higher-order dependence between instruments and disturbances. This makes this framework applicable to linear models with expectation variables that are estimated non-parametrically. Two examples of such models are the risk-return trade-off in finance and the impact of inflation uncertainty on real economic activity. Results show that inference based on Lagrange Multiplier (LM) tests is more robust to weak instruments than Wald-based inference. Using LM confidence intervals leads us to conclude that no statistically significant risk premium is present in returns on the S&P 500 index, excess holding yields between 6-month and 3-month Treasury bills, or in yen-dollar spot returns.

Environmental Risk: Should Banks Be Liable?

This paper studies the impact of banks' liability for environmental damages caused by their borrowers. Laws or court decisions that declare banks liable for environmental damages have two objectives : (1) finding someone to pay for the damages and (2) exerting a pressure on a firm's stakeholders to incite them to invest in environmental risk prevention. We study the effect that such legal decisions can have on financing relationships and especially on the incentives to reduce environmental risk in an environment where banks cannot commit to refinance the firm in all circumstances. Following an environmental accident, liable banks more readily agree to refinance the firm. We then show that bank liability effectively makes refinancing more attractive to banks, therefore improving the firm's risk-sharing possibilities. Consequently, the firm's incentives to invest in environmental risk reduction are weakened compared to the (bank) no-liability case. We also show that, when banks are liable, the firm invests at the full-commitment optimal level of risk reduction investment. If there are some externalities such that some damages cannot be accounted for, the socially efficient level of investment is greater than the privately optimal one. in that case, making banks non-liable can be socially desirable.

L'impact d'un accident vasculaire cérébral léger sur la qualité de vie des proches

Introduction : Les personnes ayant eu un accident vasculaire cérébral léger (AVCL) sont autonomes pour l’accomplissement des activités de la vie quotidienne de base, mais peuvent présenter des déficits subtils pour l’accomplissement d’activités complexes qui ne sont pas dépistés durant l’hospitalisation. Le retour à domicile parmi les proches est souvent le moment où certaines difficultés apparaissent. L’objectif de cette étude est d’explorer la perception des proches de leur qualité de vie, trois mois après le retour à domicile de la personne ayant eu l’AVCL. Méthodologie : Cette étude qualitative repose sur un paradigme constructiviste. Des entrevues individuelles semi-dirigées ont été menées à l’aide d’un guide d’entrevue, auprès de proches de personnes ayant eu un premier AVCL trois mois auparavant. Une transcription du contenu des entrevues a été faite sous forme de verbatim, suivie d’une analyse de contenu. Résultats : Huit entrevues ont été complétées (âge moyen 56,9 ans – ratio hommes/femmes de 2/6). Six thèmes ont été identifiés : personne ayant eu l’AVCL, vie quotidienne, relation conjugale, récidive, santé et services. L’AVCL engendre plusieurs incapacités (personne ayant eu l’AVCL) et les proches interrogés rapportent une augmentation de leurs tâches quotidiennes ce qui limite leurs activités personnelles (vie quotidienne). Ils mentionnent également devoir passer du rôle de conjoint à celui de parent, ce qui modifie la dynamique de couple (relation conjugale). Les proches se disent anxieux face au risque de récidive (récidive) et certains présentent des malaises physiques comme des maux de tête et un manque d’énergie (santé). Les proches affirment n’avoir accès à aucun service et que le seul soutien dont ils bénéficie est celui de leur entourage (services). Conclusion : Le retour à domicile de la personne AVCL engendre diverses conséquences sur le quotidien des proches, ce qui affecte leur qualité de vie.

Self-exclusion from health care in women at high risk for postpartum depression

Background A significant proportion of women who are vulnerable to postnatal depression refuse to engage in treatment programmes. Little is known about them, other than some general demographic characteristics. In particular, their access to health care and their own and their infants' health outcomes are uncharted. Methods We conducted a nested cohort case-control study, using data from computerized health systems, and general practitioner (GP) and maternity records, to identify the characteristics, health service contacts, and maternal and infant health outcomes for primiparous antenatal clinic attenders at high risk for postnatal depression who either refused (self-exclusion group) or else agreed (take-up group) to receive additional Health Visiting support in pregnancy and the first 2 months postpartum. Results Women excluding themselves from Health Visitor support were younger and less highly educated than women willing to take up the support. They were less likely to attend midwifery, GP and routine Health Visitor appointments, but were more likely to book in late and to attend accident and emergency department (A&E). Their infants had poorer outcome in terms of gestation, birthweight and breastfeeding. Differences between the groups still obtained when age and education were taken into account for midwifery contacts, A&E attendance and gestation;the difference in the initiation of breast feeding was attenuated, but not wholly explained, by age and education. Conclusion A subgroup of psychologically vulnerable childbearing women are at particular risk for poor access to health care and adverse infant outcome. Barriers to take-up of services need to be understood in order better to deliver care.

Drivers' ratings of different components of their own driving skill: a greater illusion of superiority for skills that relate to accident involvement

Different components of driving skill relate to accident involvement in different ways. For instance, while hazard-perception skill has been found to predict accident involvement, vehicle-control skill has not. We found that drivers rated themselves superior to both their peers and the average driver on 18 components of driving skill (N = 181 respondents). These biases were greater for hazard-perception skills than for either vehicle-control skills or driving skill in general. Also, ratings of hazard-perception skill related to self-perceived safety after overall skill was controlled for. We suggest that although drivers appear to appreciate the role of hazard perception in safe driving, any safety benefit to be derived from this appreciation may be undermined by drivers' inflated opinions of their own hazard-perception skill. We also tested the relationship between illusory beliefs about driving skill and risk taking and looked at ways of manipulating drivers' illusory beliefs.