Cost-Effectiveness of Risk-Reduction Measures from a National Viewpoint: A Case Study of the Angra Nuclear Plant in Brazil

In this paper a systemic or national approach to cost-effectiveness analysis of risk-reduction measures is reviewed, and its advantages and limitations are discussed. The method is applied to the problem of the cost-effectiveness of increasing the Angra 3 NPP containment wall thickness from the present 60 cm to 180 cm thick in order to prevent damage to the reactor core in case of a direct commercial aircraft crash on it. It is concluded that this measure is not cost-effective if the referred approach is considered.     

A Research Note on Family and Income Distribution: The Equalizing Impact of Married Women's Earnings in Metropolitan Brazil*

Using the 1985 Brazilian Annual Household Survey (PNAD), this study analyzes the extent to which the labor force participation of married women influenced familyincome inequality. The marginal impact on family earnings is decomposed into two components, one generated uniquely by differences in earnings inequality between spouses and another produced by imperfect assortive mating on spouses' earnings. Results show that the correlation among spouses' earnings is significantly less than one (0.4), and that the level of earnings inequality is more than 50 percent higher among wives than husbands. Because these forces are offsetting, wives' labor income has a negligible impact on family-income inequality in Brazil. The results demonstrate the importance of isolating the influence of imperfect assortive mating from that due to inequality in earnings between men and women.     

A Unified Approach for Calculating Core Melt Frequency Caused by Internal and External Initiating Events

A unified approach for calculating the core melt frequency of a specific reactor caused by both internal and external accident initiators is demonstrated. Two classes of internal initiators are examined: transients, of which turbine trip is the chosen example, and loss-of-coolant events of various sizes. The concepts of hazard and fragility analysis first proposed for seismic risk analysis are linked to the frequencies of internal initiating events, and to the plant response as a function of the event intensity. Uncertainties are propagated using discrete probability distribution (DPD) arithmetic. Advantages of this approach include mathematical and conceptual consistency, and an improved uncertainty analysis, which are important considerations if risk studies are to be utilized in decision-making based on quantitative safety goals.     

Prevention of Elder Mistreatment in Nursing Homes: Competencies for Direct-Care Staff

Existing training on elder mistreatment in nursing homes focuses on detection and reporting of abuse, with little training specifically targeted toward prevention of mistreatment before it occurs. We used qualitative interviews with nursing home staff, policy makers, and related professionals to identify training needs. Based on participant accounts, we drafted a number of competencies essential for caregiver training to prevent mistreatment in nursing homes. Competencies include those dealing with definitions and policies, risks for mistreatment, communication and respect in relationships with residents, and development of a cooperative work environment. Competencies are discussed along with illustrative examples, and implications for practice and policy are addressed.     

Current supply and demand for neopopulism in Latin America

The emergence of neopopulism in several countries in Latin America has been described by political science literature as the success of a political strategy in which a leader, generally bearing a strong personality and charismatic appeal, seeks popular support in an almost direct form, and overshadows political parties, the external mechanisms of control which define a democratic regime (Roberts 1996, 2003, 2006, Weyland 1999a, 1999b, Knight 1998, Connif 1999). This article adopts this definition of the concept and argues that the success of this strategy can be analyzed as a relation between supply and demand for populism. The supply refers to the political process which allows leaders to make use of features such as charisma and anti-political and polarizing speech to obtain the support of the public based on their non-programmatic and personal qualities. The demand is defined as the presence of certain preferences within the public, which increase the chance of success of the neopopulist strategy adopted by the leaders. Amidst that relationship, we can place fragile intermediate institutions, mainly the political parties, which would limit themselves to working as vehicles centered on the figure of the leader in order to allow his success among voters. Before going any deeper into the arguments regarding the relationship between supply and demand for neopopulism, the next item will discuss the concept of populism in its more contemporary form.     

Application domains for the Delta method

The Delta method uses truncated Lagrange expansions of statistics to obtain approximations to their distributions. In this paper, we consider statistics Y=g(μ+X), where X is any random vector. We obtain domains 𝒟 such that, when μ∈𝒟, we may apply the distribution derived from the Delta method. Namely, we will consider an application on the normal case to illustrate our approach.     

Approximate reference priors for Gaussian random fields

Reference priors are theoretically attractive for the analysis of geostatistical data since they enable automatic Bayesian analysis and have desirable Bayesian and frequentist properties. But their use is hindered by computational hurdles that make their application in practice challenging. In this work, we derive a new class of default priors that approximate reference priors for the parameters of some Gaussian random fields. It is based on an approximation to the integrated likelihood of the covariance parameters derived from the spectral approximation of stationary random fields. This prior depends on the structure of the mean function and the spectral density of the model evaluated at a set of spectral points associated with an auxiliary regular grid. In addition to preserving the desirable Bayesian and frequentist properties, these approximate reference priors are more stable, and their computations are much less onerous than those of exact reference priors. Unlike exact reference priors, the marginal approximate reference prior of correlation parameter is always proper, regardless of the mean function or the smoothness of the correlation function. This property has important consequences for covariance model selection. An illustration comparing default Bayesian analyses is provided with a dataset of lead pollution in Galicia, Spain.     

A probabilistic epidemiological model for infectious diseases: The case of COVID-19 at global-level

This study has developed a probabilistic epidemiological model a few weeks after the World Health Organization declared COVID-19 a pandemic (based on the little data available at that time). The aim was to assess relative risks for future scenarios and evaluate the effectiveness of different management actions for 1 year ahead. We quantified, categorized, and ranked the risks for scenarios such as business as usual, and moderate and strong mitigation. We estimated that, in the absence of interventions, COVID-19 would have a 100% risk of explosion (i.e., more than 25% infections in the world population) and 34% (2.6 billion) of the world population would have been infected until the end of simulation. We analyzed the suitability of model scenarios by comparing actual values against estimated values for the first 6 weeks of the simulation period. The results proved to be more suitable with a business-as-usual scenario in Asia and moderate mitigation in the other continents. If everything went on like this, we would have 55% risk of explosion and 22% (1.7 billion) of the world population would have been infected. Strong mitigation actions in all continents could reduce these numbers to, 7% and 3% (223 million), respectively. Although the results were based on the data available in March 2020, both the model and probabilistic approach proved to be practicable and could be a basis for risk assessment in future pandemic episodes with unknown virus, especially in the early stages, when data and literature are scarce.     

Transform orders and stochastic monotonicity of statistical functionals

In some inferential statistical methods, such as tests and confidence intervals, it is important to describe the stochastic behavior of statistical functionals, aside from their large sample properties. We study such a behavior in terms of the usual stochastic order. For this purpose, we introduce a generalized family of stochastic orders, which is referred to as transform orders, showing that it provides a flexible framework for deriving stochastic monotonicity results. Given that our general definition makes it possible to obtain some well known ordering relations as particular cases, we can easily apply our method to different families of functionals. These include some prominent inequality measures, such as the generalized entropy, the Gini index, and its generalizations. We also illustrate the applicability of our approach by determining the least favorable distribution, and the behavior of some bootstrap statistics, in some goodness-of-fit testing procedures.