Intuitive Toxicology: Expert and Lay Judgments of Chemical Risks

Human beings have always been intuitive toxicologists, relying on their senses of sight, taste, and smell to detect harmful or unsafe food, water, and air. As we have come to recognize that our senses are not adequate to assess the dangers inherent in exposure to a chemical substance, we have created the sciences of toxicology and risk assessment to perform this function. Yet despite this great effort to overcome the limitations of intuitive toxicology, it has become evident that even our best scientific methods still depend heavily on extrapolations and judgments in order to infer human health risks from animal data. Many observers have acknowledged the inherent subjectivity in the assessment of chemical risks and have indicated a need to examine the subjective or intuitive elements of expert and lay risk judgments. We have begun such an examination by surveying members of the Society of Toxicology and the lay public about basic toxicological concepts, assumptions, and interpretations. Our results demonstrate large differences between toxicologists and laypeople, as well as differences between toxicologists working in industry, academia, and government. In addition, we find that toxicologists are sharply divided in their opinions about the ability to predict a chemical's effect on human health on the basis of animal studies. We argue that these results place the problems of risk communication in a new light. Although the survey identifies misconceptions that experts should clarify for the public, it also suggests that controversies over chemical risks may be fueled as much by limitations of the science of risk assessment and disagreements among experts as by public misconceptions.     

Model averaging for treatment effect estimation in subgroups

In many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect. Estimating a treatment effect for these groups, together with an adequate uncertainty statement is challenging, owing to the resulting “random high” / selection bias. In this paper, we will investigate Bayesian model averaging to address this problem. The general motivation for the use of model averaging is to realize that subgroup selection can be viewed as model selection, so that methods to deal with model selection uncertainty, such as model averaging, can be used also in this setting. Simulations are used to evaluate the performance of the proposed approach. We illustrate it on an example early‐phase clinical trial.     

The effects of state-dependent human behavior on the design of a serial line

Most research on line design assumes that human operators perform independently from the status of the line. Recent empirical evidence is contradictory. Humans are likely to change their working speed if they might otherwise cause idle time (Schultz et al., Manage Sci 44(12):1595–1607, 1998). This peculiarity of worker behavior is observed in a variety of settings but little is still known about optimal line design that accounts for this more realistic modeling of worker behavior. Therefore, we analyze work allocation in a serial line with limited buffer capacity and adaptive human behavior. An extensive simulation study reveals that optimal work allocation in state-dependent models is different from classical state-independent models. A bowl-shaped work allocation might be suboptimal and design guidelines are more complicated. Depending on the extent of human reactions, a bowl-shaped, balanced, or reversed-bowl work allocation can be preferable.     

The Multi-dimensional Characteristics of Wellbeing: How Different Aspects of Wellbeing Interact and Do Not Interact with Each Other

Because wellbeing is a multi-varied and dynamic phenomenon, social scientists need to better understand how different aspects of people’s wellbeing are tied together and how these ties differ between individuals at one point in time and within individuals over time. The paper outlines a general model that considers the multidimensional structure of wellbeing. We utilize the potential of latent variable modelling on a unique Swedish database: the Panel Survey of Ageing and the Elderly (PSAE). An inclusive and flexible model that considers the interactions between semi-autonomous domains (material resources, health, psychosocial factors, lifestyle, etc.) is developed. Our empirical study is based on cross-sectional PSAE data and focuses on people 55 years or older (N = 5,374). The analysis takes advantage of recent developments in statistical theory in the field of latent variable modelling to bring about a more dynamic and theory confirming analysis of a multifaceted phenomenon such as wellbeing.     

State transition modeling of complex monitored health data

This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.     

Small‐sphere distributions for directional data with application to medical imaging

We propose novel parametric concentric multi‐unimodal small‐subsphere families of densities for p ? 1 ≥ 2‐dimensional spherical data. Their parameters describe a common axis for K small hypersubspheres, an array of K directional modes, one mode for each subsphere, and K pairs of concentrations parameters, each pair governing horizontal (within the subsphere) and vertical (orthogonal to the subsphere) concentrations. We introduce two kinds of distributions. In its one‐subsphere version, the first kind coincides with a special case of the Fisher–Bingham distribution, and the second kind is a novel adaption that models independent horizontal and vertical variations. In its multisubsphere version, the second kind allows for a correlation of horizontal variation over different subspheres. In medical imaging, the situation of p ? 1 = 2 occurs precisely in modeling the variation of a skeletally represented organ shape due to rotation, twisting, and bending. For both kinds, we provide new computationally feasible algorithms for simulation and estimation and propose several tests. To the best knowledge of the authors, our proposed models are the first to treat the variation of directional data along several concentric small hypersubspheres, concentrated near modes on each subsphere, let alone horizontal dependence. Using several simulations, we show that our methods are more powerful than a recent nonparametric method and ad hoc methods. Using data from medical imaging, we demonstrate the advantage of our method and infer on the dominating axis of rotation of the human knee joint at different walking phases.     

Writing resistance together

This piece of writing is a joint initiative by the participants in the Gender, Work and Organization writing workshop organized in Helsinki, Finland, in June 2019. This is a particular form of writing differently. We engage in collective writing and embody what it means to write resistance to established academic practices and conventions together. This is a form of emancipatory initiative where we care for each other as writers and as human beings. There are many author voices and we aim to keep the text open and dialogical. As such, this piece of writing is about suppressed thoughts and feelings that our collective picket line allows us to express. In order to maintain the open‐ended nature of the text, and perhaps also to retain some ‘dirtiness’ that is essential to writing, the article has not been language checked throughout by a native speaker of English.     

Quality of Life in Intentional Communities

The present study was initiated to investigate levels and predictors of wellbeing among inhabitants of intentional communities (ICs). An anonymous, Internet-based questionnaire was distributed to US and Canadian ICs. Responses were obtained from 913 members. Wellbeing was measured by the widely used satisfaction with life scale (SWLS). Hierarchical linear regression analyses were run to test the predictive validity of Social support, Identity fusion, Meaning in life, Community satisfaction and Life change, and Religious activity above and beyond demographic variables and personality traits. Mean scores on the SWLS were estimated to 5.27 and 5.47 (1–7 scale) for men and women, respectively. The full model explained 41.4% of the variance for men and 38.2% for women. Personality explained 13.4 (men) and 14.4% (women) of the variation, while demographic variables only had a minor impact. Overall, presence of Meaning in life and Social support were the most important predictors. The IC inhabitants reported wellbeing on par with the highest scores previously published. The findings support the contention that sustainability, in the form of a communal lifestyle of low ecological footprint, may be promoted without forfeiting wellbeing.     

Childlessness and Psychological Well-Being in Midlife and Old Age: An Examination of Parental Status Effects Across a Range of Outcomes

The study explores and distinguishes links between parental status (childless persons, parents with residential children, and empty nest parents) and a range of psychological well-being outcomes in midlife and old age. Data are from the first wave of the Norwegian Life Course, Ageing and Generation (NorLAG) study (n = 5,189). We separate outcomes into cognitive (life satisfaction and self-esteem) and affective (positive and negative affect, depression, loneliness) components. Parental status has a net effect on cognitive well-being among women, as childless women report significantly lower life satisfaction and self-esteem than both mothers with residential children and empty nest mothers. However, motherhood is inconsequential for affective well-being. Among men, parental status is unrelated to any of the well-being aspects. Parental status effects are not modified by age, marital status, and education. The results demonstrate the importance of investigating the effect of parental status and other objective circumstances on a range of psychological well-being outcomes. Furthermore, the results reviewed and presented indicate somewhat more positive effects of parenthood in the Nordic countries than in the US, highlighting the role of social policies in shaping the impact of parental status on well-being.