Ethnic differences in women's use of mental health services: do social networks play a role? Findings from a national survey

Objectives: The reasons for ethnic differences in women’s mental health service use in England remain unclear. The aims of this study were to ascertain: ethnic differences in women’s usage of mental health services, if social networks are independently associated with service use, and if the association between women’s social networks and service use varies between ethnic groups. Design: Logistic regression modelling of nationally representative data from the Ethnic Minority Psychiatric Illness Rates in the Community (EMPIRIC) survey conducted in England. The analytic sample (2260 women, aged 16–74 years) was drawn from the representative subsample of 2340 women in EMPIRIC for whom data on mental health services, and social networks were available. Results: Pakistani and Bangladeshi women were less likely than White women to have used mental health services (Pakistani OR = 0.23, CI = 0.08–0.65, p = .005; Bangladeshi OR = 0.25, CI = 0.07–0.86, p = .027). Frequent contact with relatives reduced mental health service use (OR = 0.45, CI = 0.23–0.89, p = .023). An increase in perceived inadequate support in women’s close networks was associated with increased odds of using mental health services (OR = 1.91, CI = 1.11–3.27, p = .019). The influence of social networks on mental health service use did not differ between ethnic groups. Conclusions: The differential treatment of women from Pakistani and Bangladeshi ethnic groups in primary care settings could be a possible reason for the observed differences in mental health service use

Multiple-membership multiple-classification models for social network and group dependences

The social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multiple-classification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications

Mental health disorders and adolescent peer relationships

Rationale: Mental health disorders often arise during adolescence, with disruptive behavior disorders and anxiety disorders among the most common. Given the salience of peer relationships during adolescence, and research suggesting that mental health disorders negatively impact social functioning, this study uses novel methodology from social network analysis to uncover the social processes linking disruptive behavior disorders and anxiety disorders with adolescent friendships. In particular, the study focuses on peer withdrawal, peer popularity, and peer homophily in relation to both disorders. Methods: Data come from 15-year old students in four Scottish secondary schools (N = 602). Diagnoses of disruptive behavior disorders and anxiety disorders were produced using the Diagnostic Interview Schedule for Children, and peer relationship data were obtained through a friendship nomination survey. Exponential random graph models were used to estimate the probability of peer withdrawal, peer popularity, and peer homophily based on each disorder. Results: Results demonstrated that adolescents with disruptive behavior disorders were more popular than their peers without disruptive behavior disorders (OR: 1.47, CI: 1.20, 1.87). Friendship was also more likely between two adolescents both with or both without disruptive behavior disorders (OR: 1.26, CI: 1.07, 1.47), demonstrating peer homophily. There was no evidence that anxiety disorders were related to adolescent peer relationships. Conclusions: Findings from this study suggest that disruptive behavior disorders may be socially rewarded (e.g., peer popularity) and socially clustered (e.g., homophily), whereas anxiety disorders show no such trends. Thus, intervention efforts must account for the peer social status that may be gained from engaging in disruptive behavior during this developmental period. Further, given that similarity in DBD status is associated with an increased likelihood of friendship, adolescents are likely to be surrounded by peers who reinforce their behaviors

The Charitable Habits of Blood Donors

Introduction: There is a need for a constant supply of blood and blood products (e.g. plasma and platelets) in the American health care system. Common recipients of blood include: patients at risk for major hemorrhage, patients with sickle cell anemia, patients undergoing surgery, and thrombocytopenia in neonatal patients. This demand is met through nationwide blood banks, such as the American Red Cross, and their blood donation programs. The American Red Cross relies solely on volunteer donors; thus, one of the most pressing issues facing this institution is getting donors in the door. Through our survey questions we hope to uncover more factors that guide individuals in their philanthropic ways. The overall goal of this research is focused on unveiling new information that will supply the American Red Cross with valuable insight into their donor population and possible opportunities for joint publicity. We investigated the similarities and difference between how and why individuals undertake certain charitable activities.https://scholarworks.uvm.edu/comphp_gallery/1206/thumbnail.jp

The effects of omitting components in a multilevel model with social network effects

Multilevel models are often used to account for the hierarchical structure of social data and the inherent dependencies to produce estimates of regression coefficients, variance components associated with each level, and accurate standard errors. Social network analysis is another important approach to analysing complex data that incoproate the social relationships between a number of individuals. Extended linear regression models, such as network autoregressive models, have been proposed that include the social network information to account for the dependencies between persons. In this article, we propose three types of models that account for both the multilevel structure and the social network structure together, leading to network autoregressive multilevel models. We investigate theoretically and empirically, using simulated data and a data set from the Dutch Social Behavior study, the effect of omitting the levels and the social network on the estimates of the regression coefficients, variance components, network autocorrelation parameter, and standard errors

Dynamic network analysis of contact diaries

Analyzing two-mode networks linking actors to events they attend may help to uncover the structure and evolution of social networks. This classic social network insight is particularly valuable in the analysis of data extracted from contact diaries where contact events produce — and at the same time are the product of relations among participants. Contact events may comprise any number of actors meeting at a specific point in time. In this paper we recall the correspondence between two-mode actor–event networks and hypergraphs, and propose relational hyperevent models (RHEM) as a general modeling framework for networks of time-stamped multi-actor events in which the diarist (“ego”) simultaneously meets several of her alters. RHEM can estimate event intensities associated with each possible subset of actors that may jointly participate in events, and test network effects that may be of theoretical or empirical interest. Examples of such effects include preferential attachment, prior shared activity (familiarity), closure, and covariate effects explaining the propensity of actors to co-attend events. Statistical tests of these effects can uncover processes that govern the formation and evolution of informal groups among the diarist’s alters. We illustrate the empirical value of RHEM using data comprising almost 2000 meeting events of former British Prime Minister Margaret Thatcher with her cabinet ministers, transcribed from contact diaries covering her first term in office (1979–1983)

Labour Market and Wider Impacts of Benefit Sanctions: A Scoping Review [Review Protocol]

The purpose of this protocol is to describe the rationale, aims, research questions and planned methodology of the scoping review. Broadly in line with Tricco et al. (2018) we have developed a detailed plan of action with a view to follow a clear, consistent and transparent process. The scoping review is envisaged as an exploratory exercise aimed at identifying and mapping existing national and international quantitative evidence on the intended and unintended impacts of benefit sanctions for people on unemployment and related benefits. This review is intended to identify the nature of the evidence base and key characteristics of studies which investigate the impacts of benefit sanctions. Furthermore, this review aims to investigate features of the research designs and methodological approaches adopted by the selected studies

The embeddedness of organizational performance: multiple membership multiple classification models for the analysis of multilevel networks

We present a Multiple Membership Multiple Classification (MMMC) model for analysing variation in the performance of organizational sub-units embedded in a multilevel network. The model postulates that the performance of organizational sub-units varies across network levels defined in terms of: (i) direct relations between organizational sub-units; (ii) relations between organizations containing the sub-units, and (iii) cross-level relations between sub-units and organizations. We demonstrate the empirical mer- its of the model in an analysis of inter-hospital patient mobility within a regional community of health care organizations. In the empirical case study we develop, organizational sub-units are departments of emergency medicine (EDs) located within hospitals (organizations). Networks within and across levels are delineated in terms of patient transfer relations between EDs (lower-level, emergency transfers), hospitals (higher-level, elective transfers), and between EDs and hospitals (cross-level, non-emergency transfers). Our main analytical objective is to examine the association of these interdependent and par- tially nested levels of action with variation in waiting time among EDs – one of the most commonly adopted and accepted measures of ED performance. We find evidence that variation in ED waiting time is associated with various components of the multilevel network in which the EDs are embedded. Before allowing for various characteristics of EDs and the hospitals in which they are located, we find, for the null models, that most of the network variation is at the hospital level. After adding these characteris- tics to the model, we find that hospital capacity and ED uncertainty are significantly associated with ED waiting time. We also find that the overall variation in ED waiting time is reduced to less than a half of its estimated value from the null models, and that a greater share of the residual network variation for these models is at the ED level and cross level, rather than the hospital level. This suggests that the covari- ates explain some of the network variation, and shift the relative share of residual variation away from hospital networks. We discuss further extensions to the model for more general analyses of multilevel network dependencies in variables of interest for the lower level nodes of these social structures