Sex, ethnic and socioeconomic inequalities and trajectories in child and adolescent mental health in Australia and the UK: findings from national prospective longitudinal studies

BACKGROUND: This study investigates the sex, ethnic and socioeconomic inequalities in emotional difficulties over childhood and adolescence using longitudinal cohort studies in the UK and Australia. Estimating cross-national differences contributes to understanding of the consistency of inequalities in mental health across contexts. METHODS: Data from 19,748 participants in two contemporary representative samples in Australia (Growing Up in Australia: The Longitudinal Study of Australian Children, n = 4,975) and UK (Millennium Cohort Study, n = 14,773) were used. Emotional difficulties were assessed using the parent-reported Strengths and Difficulties Questionnaire at ages 4/5, 6/7, 11/12 and 14/15 years and the self-reported Short Moods and Feelings Questionnaire at age 14/15. Latent Growth Curve Modelling was used to examine mental health over time. RESULTS: There were significant increases in emotional difficulties in both countries over time. Emotional difficulties were higher in Australian children at all ages. The gender gap in self-reported depressive symptoms at age 14/15 was larger in the UK (8% of UK and 13% of Australian boys were above the depression cut-off, compared with 23% of girls). Ethnic minority children had higher emotional difficulties at age 4/5 years in both countries, but over time this difference was no longer observed in Australia. In the UK, this reversed whereby at ages 11/12 and 14/15 ethnic minority children had lower symptoms than their White majority peers. Socioeconomic differences were more marked based on parent education and employment status in Australia and by parent income in the UK. UK children, children from White majority ethnicity and girls evidenced steeper worsening of symptoms from age 4/5 to 14/15 years. CONCLUSIONS: Even in two fairly similar countries (i.e. English-speaking, high-income, industrialised), the observed patterns of inequalities in mental health symptoms based on sociodemographics are not the same. Understanding country and context-specific drivers of different inequalities provides important insights to help reduce disparities in child and adolescent mental health

Spatial correlations in attribute communities

Community detection is an important tool for exploring and classifying the properties of large complex networks and should be of great help for spatial networks. Indeed, in addition to their location, nodes in spatial networks can have attributes such as the language for individuals, or any other socio-economical feature that we would like to identify in communities. We discuss in this paper a crucial aspect which was not considered in previous studies which is the possible existence of correlations between space and attributes. Introducing a simple toy model in which both space and node attributes are considered, we discuss the effect of space-attribute correlations on the results of various community detection methods proposed for spatial networks in this paper and in previous studies. When space is irrelevant, our model is equivalent to the stochastic block model which has been shown to display a detectability-non detectability transition. In the regime where space dominates the link formation process, most methods can fail to recover the communities, an effect which is particularly marked when space-attributes correlations are strong. In this latter case, community detection methods which remove the spatial component of the network can miss a large part of the community structure and can lead to incorrect results.Comment: 10 pages and 7 figure

Modelling hepatitis C transmission over a social network of injecting drug users

Hepatitis C virus (HCV) is a blood-borne virus that disproportionately affects people who inject drugs (PWIDs). Based on extensive interview and blood test data from a longitudinal study in Melbourne, Australia, we describe an individual-based transmission model for HCV spread amongst PWID. We use this model to simulate the transmission of HCV on an empirical social network of PWID. A feature of our model is that sources of infection can be both network neighbours and non-neighbours via "importing". Data-driven estimates of sharing frequency and rate of importing are provided. Compared to an appropriately calibrated fully connected network, the empirical network provides some protective effect on the time to primary infection. We also illustrate heterogeneities in incidence rate of infection, both across and within node degrees (i.e., number of network partners). We explore the reduced risk of infection from spontaneously clearing cutpoint nodes whose infection status oscillates, both in theory and in simulation. Further, we show our model-based estimate of per-event transmission probability largely agrees with previous estimates at the lower end of the range 1–3% commonly cited