Problematic Eating and Food-related Behaviours and Excessive Weight Gain: Why Children in Out-of-home Care Are at Risk

Emerging evidence suggests that abuse and neglect in childhood may play a role in subsequent development of obesity. One population group particularly at risk is children and young people living in out-of-home care (OOHC). Given this population is already a vulnerable group, identifying potential mechanisms by which childhood abuse and neglect increases risk for obesity is essential. A possible explanation is that problematic eating and food-related behaviours (i.e., emotional eating, compulsive eating, overeating, binge eating, stealing or hoarding food) might mediate the association between adverse childhood experiences and obesity. Hence, the overall goal of this paper was to provide a narrative review of eating and food-related difficulties for children in care and their possible association with unhealthy and excessive weight gain. This review revealed a shortage of existing empirical papers and signalled particular need for further examination of the mediating effects of problematic eating.     

‘A Hidden Group of Children’: Support in Schools for Children who Experience Parental Imprisonment

Schools have been identified as playing a key role in supporting the children of prisoners. This paper reports on a study, which explored the support provision offered in schools to children who experience parental imprisonment. By interviewing school representatives, stakeholders, parents and children, we illustrate the support available in schools, the issues that arise and ways in which support provision can be strengthened. Our findings indicate that children of prisoners often constitute a ‘forgotten’ group in schools, and we suggest that an awareness of these children and the challenges they face needs to be raised amongst education practitioners and policy‐makers.     

Discrete‐time survival trees

Tree‐based methods are frequently used in studies with censored survival time. Their structure and ease of interpretability make them useful to identify prognostic factors and to predict conditional survival probabilities given an individual's covariates. The existing methods are tailor‐made to deal with a survival time variable that is measured continuously. However, survival variables measured on a discrete scale are often encountered in practice. The authors propose a new tree construction method specifically adapted to such discrete‐time survival variables. The splitting procedure can be seen as an extension, to the case of right‐censored data, of the entropy criterion for a categorical outcome. The selection of the final tree is made through a pruning algorithm combined with a bootstrap correction. The authors also present a simple way of potentially improving the predictive performance of a single tree through bagging. A simulation study shows that single trees and bagged‐trees perform well compared to a parametric model. A real data example investigating the usefulness of personality dimensions in predicting early onset of cigarette smoking is presented. The Canadian Journal of Statistics 37: 17‐32; 2009 © 2009 Statistical Society of Canada     

Adolescents at risk of mental health problems in the COVID-19 pandemic: A prospective population-based study of the effects of government mandates and school closures

There is increasing evidence that the COVID-19 pandemic has had substantial mental health impacts for adolescents. Yet, few definitive studies have investigated which adolescents were at higher risk of poor mental health and well-being during the pandemic. Data were drawn from the Childhood to Adolescence Transition Study, a prospective cohort study of students in Australia (N = 1211). Prevalence of mental health outcomes (depressive symptoms, anxiety symptoms, self-harm and good subjective well-being) was estimated in school Years 5–12, where Years 11 (2020) and 12 (2021) coincided with the pandemic. The age- and sex-adjusted relative risk of each mental health outcome for each priority group during the pandemic were estimated. During the pandemic, over 50% of study participants reported depressive symptoms, and one quarter reported anxiety symptoms. There was a decrease in good subjective well-being compared with pre-pandemic years, while self-harm prevalence remained similar. History of mental health problems, school disengagement and frequent peer victimisation increased the risk of experiencing mental health problems during the pandemic. Schools play a central role in maintaining the mental health and good subjective well-being of students, and this is particularly important during periods of social disruption, such as the COVID-19 pandemic.     

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.