Geographic location or experience: Using PRISM to understand how people seek flood risk information

Flooding is increasing worldwide, and with climate change, people need help understanding these changing conditions and that their flood risk may also change. This study extends the planned risk information seeking model (PRISM) into the flood risk domain and examines the antecedents that explain flood risk information seeking behavior. Using a survey reflective of the population in the state of Texas (N = 1079), this study includes an operationalization of risk perception specific to the complexity of floods and explores two key moderators in the PRISM model. Findings suggest that using PRISM to elaborate flood risk information seeking behaviors explains 48% of the variance in information seeking intent and 37% of the variance in affective risk perception. Using multigroup modeling, the findings also reveal that simply living in an area at high risk for floods does not significantly impact any relationships in the model. However, having experience with flooding increases the strength of risk perception paths—in particular, perceived probability of flood risk—and better explains flood risk information seeking. Suggestions for how to use communication to influence risk perceptions and information seeking, as well as future directions for research, are also discussed.     

Bayesian modelling of effects of prenatal alcohol exposure on child cognition based on data from multiple cohorts

High levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, but the exact nature of the dose-response relationship is less well understood. To investigate this relationship, data were assembled from six longitudinal birth cohort studies examining the effects of PAE on cognitive outcomes from early school age through adolescence. Structural equation models (SEMs) are a natural approach to consider, because of the way they conceptualise multiple observed outcomes as relating to an underlying latent variable of interest, which can then be modelled as a function of exposure and other predictors of interest. However, conventional SEMs could not be fitted in this context because slightly different outcome measures were used in the six studies. In this paper we propose a multi-group Bayesian SEM that maps the unobserved cognition variable to a broad range of observed outcomes. The relation between these variables and PAE is then examined while controlling for potential confounders via propensity score adjustment. By examining different possible dose-response functions, the proposed framework is used to investigate whether there is a threshold PAE level that results in minimal cognitive deficit.