Gendering Indicators of Health and Well-being: Is Quality of Life Gender Neutral?

The addition of social indicators and quality of life measures to the raft of traditional health indicators used to assess health and well-being has certainly provided a much-needed contextual understanding of health outcomes. However, most quality of life measures remain undifferentiated by gender. Outcomes can be disaggregated along age, class, ethnic, racial and gender dimensions but few quality of life measures (or social indicators for that matter) are sensitive to the subtle effects of gender socialization on health and well-being. Both social epidemiology and quality of life measures need to be gendered and differentiated to fully capture the diversity of womens and mens health experiences.     

Bayesian non-parametric modeling for integro-difference equations

Integro-difference equations (IDEs) provide a flexible framework for dynamic modeling of spatio-temporal data. The choice of kernel in an IDE model relates directly to the underlying physical process modeled, and it can affect model fit and predictive accuracy. We introduce Bayesian non-parametric methods to the IDE literature as a means to allow flexibility in modeling the kernel. We propose a mixture of normal distributions for the IDE kernel, built from a spatial Dirichlet process for the mixing distribution, which can model kernels with shapes that change with location. This allows the IDE model to capture non-stationarity with respect to location and to reflect a changing physical process across the domain. We address computational concerns for inference that leverage the use of Hermite polynomials as a basis for the representation of the process and the IDE kernel, and incorporate Hamiltonian Markov chain Monte Carlo steps in the posterior simulation method. An example with synthetic data demonstrates that the model can successfully capture location-dependent dynamics. Moreover, using a data set of ozone pressure, we show that the spatial Dirichlet process mixture model outperforms several alternative models for the IDE kernel, including the state of the art in the IDE literature, that is, a Gaussian kernel with location-dependent parameters.     

A preliminary review of the parenting assessment and skill development service: A 10-day residential service for families at risk of child maltreatment

Tweddle's Parenting Assessment and Skill Development Service (PASDS) is a 10-day residential programme for families at risk of child maltreatment. The service aims to conduct a comprehensive parenting capacity assessment to inform case management and improve parenting practices. The aims of the study were to (1) describe the psychosocial functioning of parents taking part in PASDS, (2) describe the key parenting assessment outcomes, and (3) explore parents' experiences of participation in PASDS including (a) perceived barriers and enablers of participation and (b) perceived outcomes for their family. Participants were 18 parents who participated in Tweddle's PASDS in Melbourne, Australia. A mixed methods study comprising a case file review and qualitative interviews was conducted. Parents accessing PASDS had experienced adverse childhood events, and many were experiencing mental health difficulties. Interviews with parents revealed perceived benefits of PASDS in improving parenting knowledge and skills, family relationships, and parenting confidence. At the time of discharge, 33% of families were assessed as unable to provide independent care for their child. The current findings have important implications for the further evaluation of evidenced-based services which can improve the accuracy of parenting capacity assessments, aiding in child protection decision-making related to child placement and safety.     

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.