Understanding Local Environmental Concern: The Importance of Place

Urban‐rural differences in environmental concern are the primary way that place has been conceptualized within the social bases of environmental concern framework, yet there has been little convergence in empirical findings to support such differences. We assess the influence of place of permanent residence and other sociodemographic measures of the social bases of environmental concern approach alongside two social‐psychological constructs: place attachment and place outlook. Our work focuses on second homeowners in three rural, natural amenity communities of the northeastern United States (n = 405). Second homeowners who permanently reside in rural places exhibited lower levels of local environmental concern about their second home area than suburban and urban residents, when “rural” was defined at the county scale. We did not observe differences in local environmental concern based upon urban‐suburban‐rural permanent residence when place of permanent residence was defined at the tract, block group, or zip code levels. Place attachment and place outlook explain more variance in local environmental concern than all sociodemographic indicators combined. Our findings suggest that second homeowners' local environmental concern is not strongly or consistently shaped by the urbanity or rurality of their permanent residence, but that place‐based, social‐psychological constructs may offer mechanisms through which social‐structural forces shape environmental concern.     

Mapping social work education in the West Africa region: Movements toward indigenization in 12 countries’ training programs

This article presents the results of a systematic mapping of social work training programs in countries throughout West Africa, a region historically under‐represented in global discussions of the social welfare workforce. The research illuminates how social workers and related professionals are trained to engage in social work practice in a number of West African countries. The research was conducted in two phases. In the initial phase, the research team collected documents from 12 West African countries and conducted phone interviews with relevant individuals. The second phase included field research in five West African countries ? Burkina Faso, Côte d’Ivoire, Ghana, Nigeria, and Senegal ? where the research team conducted semi‐structured interviews and group discussions with 253 individuals. Framed by indigenization theory, this study describes social service training institutes in West Africa and highlights the varying degrees to which programs have been adapted to indigenous and endogenous realities in the postcolonial era.     

A Quantitative Microbial Risk Assessment Model for Legionnaires'' Disease: Animal Model Selection and Dose-Response Modeling

Legionnaires' disease (LD), first reported in 1976, is an atypical pneumonia caused by bacteria of the genus Legionella, and most frequently by L. pneumophila (Lp). Subsequent research on exposure to the organism employed various animal models, and with quantitative microbial risk assessment (QMRA) techniques, the animal model data may provide insights on human dose-response for LD. This article focuses on the rationale for selection of the guinea pig model, comparison of the dose-response model results, comparison of projected low-dose responses for guinea pigs, and risk estimates for humans. Based on both in vivo and in vitro comparisons, the guinea pig (Cavia porcellus) dose-response data were selected for modeling human risk. We completed dose-response modeling for the beta-Poisson (approximate and exact), exponential, probit, logistic, and Weibull models for Lp inhalation, mortality, and infection (end point elevated body temperature) in guinea pigs. For mechanistic reasons, including low-dose exposure probability, further work on human risk estimates for LD employed the exponential and beta-Poisson models. With an exposure of 10 colony-forming units (CFU) (retained dose), the QMRA model predicted a mild infection risk of 0.4 (as evaluated by seroprevalence) and a clinical severity LD case (e.g., hospitalization and supportive care) risk of 0.0009. The calculated rates based on estimated human exposures for outbreaks used for the QMRA model validation are within an order of magnitude of the reported LD rates. These validation results suggest the LD QMRA animal model selection, dose-response modeling, and extension to human risk projections were appropriate.     

Regression models for analyzing clustered binary and continuous outcomes under an assumption of exchangeability

Scientific experiments commonly result in clustered discrete and continuous data. Existing methods for analyzing such data include the use of quasi-likelihood procedures and generalized estimating equations to estimate marginal mean response parameters. In applications to areas such as developmental toxicity studies, where discrete and continuous measurements are recorded on each fetus, or clinical ophthalmologic trials, where different types of observations are made on each eye, the assumption that data within cluster are exchangeable is often very reasonable. We use this assumption to formulate fully parametric regression models for clusters of bivariate data with binary and continuous components. The regression models proposed have marginal interpretations and reproducible model structures. Tractable expressions for likelihood equations are derived and iterative schemes are given for computing efficient estimates (MLEs) of the marginal mean, correlations, variances and higher moments. We demonstrate the use the ‘exchangeable’ procedure with an application to a developmental toxicity study involving fetal weight and malformation data.