An Index of Child Well-being in the European Union

While the living conditions of children and young people in the European Union have gained increasing recognition across the EU, the well-being of children is not monitored on the European level. Based on a rights-based, multi-dimensional understanding of child well-being we analyse data already available for the EU 25, using series data as well as comparative surveys of children and young people. We compare the performance of EU Member States on eight clusters with 23 domains and 51 indicators and give a picture of children’s overall well-being in the European Union. The clusters are children’s material situation, housing, health, subjective well-being, education, children’s relationships, civic participation and risk and safety.     

Into the Community: A Comparison of Care Management and Traditional Approaches to Resettlement1

The resettlement of people with learning disabilities (mental handicap) from long-stay hospitals has been under way for a number of years in England. This process has generally been dominated by the exigencies of the available services. A needs-led approach, emphasized in recent community care legislation, in which new services are sought on the basis of the specific requirements of service users, has been used infrequently for this purpose. This paper compares the process of implementing a needs-led care management approach to resettlement with the implementation of a more traditional service-led approach. It also suggests some important lessons for the application of a needs-led approach to hospital resettlement.     

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.     

‘We can play tag with a stick’. Children's knowledge,experiences, feelings and creative thinking during the COVID-19 pandemic

Using a relational approach, we draw on repeated interviews with a group of 30 diverse children from Ontario to share and reflect on their knowledge, experiences and feelings early in the COVID-19 pandemic. Prioritising relational interdependence and relational agency, this paper illustrates our participants' embedded engagements with the pandemic and their contribution to the co-production of knowledge. We emphasise their thoughtful responses to the pandemic; their creative, self-reflexive strategies for managing a difficult time; and their advice to others. We thus prioritise children's viewpoints and emphasise their relational interconnections with others during a time that was marked by social isolation.     

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.