Parent Marital Quality and the Parent–Adolescent Relationship: Effects on Adolescent and Young Adult Health Outcomes

Although a number of studies examined the implications of marital disruption for adolescent well-being, few studied the implications of marital relationship quality on health outcomes for children in married-couple families. The present study examines how parent marital quality among intact families interacts with the quality of parent–adolescent relationships to predict physical health, mental health, and substance use in middle adolescence and early adulthood. The study uses data from the NLSY97 cohort, a nationally representative sample of adolescents who are being followed into adulthood. Predictors include the quality of the parent marital relationship, the quality of the parent–adolescent relationship, marital structure, and a number of contextual covariates and control variables. Combined parent marital quality and parent–adolescent relationship groups were developed using latent class analyses and were used to predict positive and negative health behaviors during the teen and early adult years. Results indicate that adolescents in families experiencing poor marital quality fared worse on physical health, mental health, and substance use outcomes. In addition, adolescents who reported poor relationships with at least one of their parents fared worse on outcomes. Adolescents whose parents have low-quality relationships and also have poor parent–adolescent relationships tended to fare least well across health measures. Adolescents whose parents have a high-quality relationship and who have a good parent–adolescent relationship with both parents consistently had the best outcomes. Overall, poor relationships consistently undermine mental health, physical health, and substance use. Family religious activities also consistently predict better health outcomes.     

Inside Out: An ‘English’ Dispersal into North-West Wales

This article is based on an empirical baseline study undertaken in 2005 which examined the experience of ‘the English’ who have moved and settled into north-west Wales. The movement of the British/white groups and their experiences have been less of a subject for examination and yet a dispersal of ‘the English’/British as both immigrants and colonisers has occurred and is ongoing. Migration between the constituent countries of the UK and the movement of English people into Wales has been a long-standing and accepted practice; it has been resisted by some in the more Welsh parts of Wales as a continuing ‘colonisation’. The rise of devolution within the UK and the burgeoning Welsh Assembly Government (WAG) with its increasing powers for self governance is leading to a decline of English dominance in Wales. This essay explores the experiences of ‘the English’ in Wales as England's influence over Wales becomes diluted, the role of leader and led reversed, and as the ‘empire begins to bite back’ (with apologies to Paul Gilroy).     

A DIRICHLET PROCESS MIXTURE OF HIDDEN MARKOV MODELS FOR PROTEIN STRUCTURE PREDICTION

By providing new insights into the distribution of a protein's torsion angles, recent statistical models for this data have pointed the way to more efficient methods for protein structure prediction. Most current approaches have concentrated on bivariate models at a single sequence position. There is, however, considerable value in simultaneously modeling angle pairs at multiple sequence positions in a protein. One area of application for such models is in structure prediction for the highly variable loop and turn regions. Such modeling is difficult due to the fact that the number of known protein structures available to estimate these torsion angle distributions is typically small. Furthermore, the data is "sparse" in that not all proteins have angle pairs at each sequence position. We propose a new semiparametric model for the joint distributions of angle pairs at multiple sequence positions. Our model accommodates sparse data by leveraging known information about the behavior of protein secondary structure. We demonstrate our technique by predicting the torsion angles in a loop from the globin fold family. Our results show that a template-based approach can now be successfully extended to modeling the notoriously difficult loop and turn regions.     

Global value chains for medical gloves during the COVID-19 pandemic: Confronting forced labour through public procurement and crisis

This paper evaluates ways in which labour issues in global value chains for medical gloves have been affected by, and addressed through, the COVID-19 pandemic. It focuses on production in Malaysia and supply to the United Kingdom's National Health Service and draws on a large-scale survey with workers and interviews with UK government officials, suppliers and buyers. Adopting a Global Value Chain (GVC) framework, the paper shows how forced labour endemic in the sector was exacerbated during the pandemic in the context of increased demand for gloves. Attempts at remediation are shown to operate through both a reconfigured value chain in which power shifted dramatically to the manufacturers and a context where public procurement became higher in profile than ever before. It is argued that the purchasing power of governments must be leveraged in ways that more meaningfully address labour issues, and that this must be part of value chain resilience.     

Statistical estimation and comparison of group-specific bivariate correlation coefficients in family-type clustered studies

Bivariate correlation coefficients (BCCs) are often calculated to gauge the relationship between two variables in medical research. In a family-type clustered design where multiple participants from same units/families are enrolled, BCCs can be defined and estimated at various hierarchical levels (subject level, family level and marginal BCC). Heterogeneity usually exists between subject groups and, as a result, subject level BCCs may differ between subject groups. In the framework of bivariate linear mixed effects modeling, we define and estimate BCCs at various hierarchical levels in a family-type clustered design, accommodating subject group heterogeneity. Simplified and modified asymptotic confidence intervals are constructed to the BCC differences and Wald type tests are conducted. A real-world family-type clustered study of Alzheimer disease (AD) is analyzed to estimate and compare BCCs among well-established AD biomarkers between mutation carriers and non-carriers in autosomal dominant AD asymptomatic individuals. Extensive simulation studies are conducted across a wide range of scenarios to evaluate the performance of the proposed estimators and the type-I error rate and power of the proposed statistical tests.Abbreviations: BCC: bivariate correlation coefficient; BLM: bivariate linear mixed effects model; CI: confidence interval; AD: Alzheimer’s disease; DIAN: The Dominantly Inherited Alzheimer Network; SA: simple asymptotic; MA: modified asymptoticKEYWORDS: Bivariate correlation coefficient, bivariate linear mixed effects model, parameter estimation, confidence interval, hypothesis testing, type-I error/size and power     

Cumulative Social-Environmental Adversity Exposure as Predictor of Psychological Distress and Risk Behavior in Urban Youth

The present study examines exposure to adversity as a predictor of psychological distress and risk behavior among 266 elementary students in 14 low income neighborhood schools in Chicago. Two indices of adversity exposure were created, reflecting neighborhood, school, and peer group risk (NSP) and the lack of protective resources within the family and neighborhood (LPR). Regression analyses investigated these indices as independent and potentially interacting predictors of self-reported symptoms of depression and anxiety, frequency of substance use, and involvement in violence-related behavior. The LPR index was found to be a linear predictor of greater reported anxiety and exhibited a significant curvilinear association with reports of depression, substance use, and violence-related behavior. Similarly, the NSP index was a linear predictor of greater reported levels of substance use and violence-related behavior. Significant interaction between the two indices was found. Implications for research and school clinicians (social workers and psychologists) are offered.     

Complex demoeconomic dynamics

We show how various kinds of qualitative long run demoeconomic behavior depend on household preferences, productivity and the cost of childrearing. In particular, a dominant trend in growth can be interspersed with periods of fluctuation as slowly moving changes in productivity, preference and child care costs push nonlinearities past crucial bifurcation points. Moreover, nonperiodic, essentially unpredictable demoeconomic behavior is robust, thus suggesting a possible explanation for persistent and substantial errors in population forecasts.[/p]A partial draft of the present paper was first presented at the annual meetings of the Population Association of America, San Francisco, April 3, 1986. The computer experiments were performed by Changseob Kim and Weihong Huang.