The Methuselah Effect: The Pernicious Impact of Unreported Deaths on Old-Age Mortality Estimates

We examine inferences about old-age mortality that arise when researchers use survey data matched to death records. We show that even small rates of failure to match respondents can lead to substantial bias in the measurement of mortality rates at older ages. This type of measurement error is consequential for three strands in the demographic literature: (1) the deceleration in mortality rates at old ages; (2) the black-white mortality crossover; and (3) the relatively low rate of old-age mortality among Hispanics, often called the “Hispanic paradox.” Using the National Longitudinal Survey of Older Men matched to death records in both the U.S. Vital Statistics system and the Social Security Death Index, we demonstrate that even small rates of missing mortality matching plausibly lead to an appearance of mortality deceleration when none exists and can generate a spurious black-white mortality crossover. We confirm these findings using data from the National Health Interview Survey matched to the U.S. Vital Statistics system, a data set known as the “gold standard” (Cowper et al. 2002) for estimating age-specific mortality. Moreover, with these data, we show that the Hispanic paradox is also plausibly explained by a similar undercount.     

Health responses to a wealth shock: evidence from a Swedish tax reform

This paper makes two contributions to the literature on the effects of wealth on health. First, it deals with reverse causality and omitted variable bias by exploiting exogenous variation in inherited wealth generated by the repeal of the Swedish inheritance tax. Second, it analyzes responses in health outcomes through the use of administrative registers. The results show that increased wealth has limited short to medium run impacts on objective adult health. This is in line with what has previously been reported in the literature.     

The Use of Simulation to Reduce the Domain of “Black Swans” with Application to Hurricane Impacts to Power Systems

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts.     

Risky Business: Is There an Association between Casual Sex and Mental Health among Emerging Adults?

A multiethnic sample of single, heterosexual, emerging-adult college students (N = 3,907) ages 18 to 25, from 30 institutions across the United States, participated in a study about identity, culture, psychological well-being, and risky behaviors. Given ongoing debates about the connection between casual sex and psychological adjustment, in the current study we assessed the cross-sectional association of participation in casual sex with psychological well-being and distress. A greater proportion of men (18.6%) compared to women (7.4%) reported having had casual sex in the month prior to assessment. Structural equation modeling indicated that casual sex was negatively associated with well-being (ß = .20, p < .001) and positively associated with psychological distress (ß = .16, p < .001). Gender did not moderate these associations. For emerging-adult college students, engaging in casual sex may elevate risk for negative psychological outcomes.     

Control centers design for ergonomics and safety

This paper shows the general design conditions about ergonomics and safety for control centers in the petrochemical process industry. Some of the topics include guidelines for the optimized workstation design, control room layout, building layout, and lighting, acoustical and environmental design. Also takes into account the safety parameters in the control rooms and centers design. The conditions and parameters shown in this paper come from the standards and global advances on this topic on the most recent publications. And also the work was supplemented by field visits of our team to the control center operations in a petrochemical company, and technical literature search efforts. This guideline will be useful to increase the productivity and improve the working conditions at the control rooms.     

Risk Analysis for U.S. Offshore Wind Farms: The Need for an Integrated Approach

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near‐zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large‐scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on‐going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long‐term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this “Perspective”, we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on‐going research on the risks from wind farms.     

Social perception of natural risks by local residents in developing countries—The example of the coastal area of Benin

Risk management and planning activities cannot be sustainably and efficiently implemented unless being based on a participative approach resulting from the problem consciousness and perception of the local inhabitants. This requires that the measures linked to problem perception and assessment by local stakeholders, above all by the population affected, are known. This investigation conducted in the flat Beninese coastal lagoon areas aims to assess the local inhabitants’ risk perception. The results are the following: (i) the stakeholders have group-specific ways of risk perception (according to ethnicity, social group, age); (ii) every risk management strategy should be based on the group-specific ways of risk perception and assessment; (iii) the acceptance of a given risk management strategy including interactive ways of participation can be advanced through education, dissemination of risk information as well as through communication between stakeholders.     

Extension of the Application of Conway‐Maxwell‐Poisson Models: Analyzing Traffic Crash Data Exhibiting Underdispersion

The objective of this article is to evaluate the performance of the COM‐Poisson GLM for analyzing crash data exhibiting underdispersion (when conditional on the mean). The COM‐Poisson distribution, originally developed in 1962, has recently been reintroduced by statisticians for analyzing count data subjected to either over‐ or underdispersion. Over the last year, the COM‐Poisson GLM has been evaluated in the context of crash data analysis and it has been shown that the model performs as well as the Poisson‐gamma model for crash data exhibiting overdispersion. To accomplish the objective of this study, several COM‐Poisson models were estimated using crash data collected at 162 railway‐highway crossings in South Korea between 1998 and 2002. This data set has been shown to exhibit underdispersion when models linking crash data to various explanatory variables are estimated. The modeling results were compared to those produced from the Poisson and gamma probability models documented in a previous published study. The results of this research show that the COM‐Poisson GLM can handle crash data when the modeling output shows signs of underdispersion. Finally, they also show that the model proposed in this study provides better statistical performance than the gamma probability and the traditional Poisson models, at least for this data set.