A Risk Analysis for Airborne Pathogens with Low Infectious Doses: Application to Respirator Selection Against Coccidioides immitis Spores

Probability models incorporating a deterministic versus stochastic infectious dose are described for estimating infection risk due to airborne pathogens that infect at low doses. Such pathogens can be occupational hazards or candidate agents for bioterrorism. Inputs include parameters for the infectious dose model, distribution parameters for ambient pathogen concentrations, the breathing rate, the duration of an exposure period, the anticipated number of exposure periods, and, if a respirator device is used, distribution parameters for respirator penetration values. Application of the models is illustrated with a hypothetical scenario involving exposure to Coccidioides immitis, a fungus present in soil in areas of the southwestern United States Inhaling C. immitis spores causes a respiratory tract infection and is a recognized occupational hazard in jobs involving soil dust exposure in endemic areas An uncertainty analysis is applied to risk estimation in the context of selecting respiratory protection with a desired degree of efficacy.     

Disentangling the exposure experience: the roles of community context and report-back of environmental exposure data

This article examines participants' responses to receiving their results in a study of household exposure to endocrine disrupting compounds and other pollutants. The authors study how the "exposure experience"-the embodied, personal experience and understanding of chronic exposure to environmental pollutants-is shaped by community context and the report-back process itself. In addition, the authors investigate an activist, collective form of exposure experience. The authors analyze themes of expectations and learning, trust, and action. The findings reveal that while participants interpret scientific results to affirm lay knowledge of urban industrial toxics, they also absorb new information regarding other pollutant sources. By linking the public understanding of science literature to the illness and exposure experience concepts, this study unravels the complex relationship between lay experience and lay understanding of science. It also shows that to support policy development and/or social change, community-based participatory research efforts must attend to participants' understanding of science.     

Modeling oil price fluctuations and international stockpile coordination

Major interruptions in the supply of crude oil in the Middlle East have caused significant economic damage in terms of lost output and incresed inflation in the industrial countries. To the extent that the macroeconomic costs of shocks are a function of the magnitude of the oil price increases, domestic or internationally coordinated policies to restrain oil price increases during disruptions can be beneficial. One such policy initiative is the release of oil held in public stockpiles. We address the motivations for private and public stockpilling in an intertemporal optimizing model. As a special case of our general model, we develop and simulate a model of the world oil market to examine the benefits (in terms of lower world oil prices) of releasing oil from the U.S. Strategic Petroleum Reserve.     

The Relations among Observational, Physiological, and Self-Report Measures of Children's Anger

Our first goal was to examine the relations among observational, physiological, and self‐report measures of children's anger. Our second goal was to investigate whether these relations varied by reactive or proactive aggression. Children (272 second‐grade boys and girls) participated in a procedure in which they lost a game and prize to a confederate who cheated. Skin conductance reactivity, heart rate reactivity, self‐reports of anger, angry facial expressions, and angry nonverbal behaviors were measured for each turn of the game. We used multi‐level regressions to calculate the relations among the 10 pairs of the five anger variables over the course of the game. Six of the 10 pairs of anger variables were positively related. These findings suggest that measuring children's anger using any one approach may not capture the full complexity of children's overall experience and expression of anger. Furthermore, three of the 10 relations were stronger at higher levels of reactive aggression, although none varied by proactive aggression. These findings suggest that reactive aggression is related to greater cohesiveness in the experience and expression of anger than is proactive aggression.     

Estimating detailed distributions from grouped sociodemographic data: ‘get me started in’ curve fitting using nonlinear models

In much demographic analysis, it is important to know how occurrence-exposure rates or transition probabilities vary continuously by age or by time. Often we have coarse or fluctuating data so there can be a need for estimation and smoothing. Since the distributions of rates or counts across age or another variable are often curved, a nonlinear model is likely to be appropriate. The main focus of this paper is on the estimation of detailed information from grouped data such as age and income bands; however, the methods we outline could also be applied to other settings such as smoothing rates where the original data are ragged. The ability to carry out curve fitting is a very useful skill for population geographers and demographers. Curve fitting is not well covered in statistics textbooks, and whilst there is a large literature in journals thoroughly discussing the detail of functions which define curves, these texts are likely to be inaccessible to researchers who are not specialists in mathematics. We aim here to make nonlinear modelling as accessible as possible. We demonstrate how to carry out nonlinear regression using SPSS, giving stepped-through hypothetical and research examples. We note other software in which nonlinear regression can be carried out, and outline alternative methods of curve fitting.