The Eating Disorders NOS Diagnostic Profile Among College Women

Abstract

The authors examined a proposed profile of eating-related behaviors, associated features, developmental issues, and help-seeking behavior among college women, using an eating disorder response program. The most common symptom scenario was a pattern of regular binge eating, together with daily exercise and occasional purging. The most common associated features were distressing or dysfunctional overconcern about body image and self-esteem, usually with day-to-day stress and intermittent depression. The women who fit this pattern also presented developmental issues of perfectionism, conflictual relationships with parents, and struggles for independence; and they tended to be ambivalent about seeking services. Implications for practice, including the need to develop a framework for eating disorder responses on campus that includes preventive programs and developmental interventions to target emerging and moderate concerns are discussed; limitations and the preliminary nature of the findings are explicated.     

Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence

Most existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this article, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables. This article has online supplementary material.     

Investigating impacts of complex sampling on latent growth curve modelling

We investigate the impacts of complex sampling on point and standard error estimates in latent growth curve modelling of survey data. Methodological issues are illustrated with empirical evidence from the analysis of longitudinal data on life satisfaction trajectories using data from the British Household Panel Survey, a national representative survey in Great Britain. A multi-process second-order latent growth curve model with conditional linear growth is used to study variation in the two perceived life satisfaction latent factors considered. The benefits of accounting for the complex survey design are considered, including obtaining unbiased both point and standard error estimates, and therefore correctly specified confidence intervals and statistical tests. We conclude that, even for the rather elaborated longitudinal data models that were considered, estimation procedures are affected by variance-inflating impacts of complex sampling.     

Employment status of women with disabilities from the Behavioral Risk Factor Surveillance Survey (1995-2002)

Among working aged adults (18-64) with disabilities, three out of 10 (32%) work full or part-time, compared to eight out of 10 (81%) of those without disabilities [9]. In addition, 24.7% of women with a severe disability and 27.8% of men with a severe disability are employed, while women with a non-severe disability have an employment rate of 68.4% and men with a non-severe disability have an employment rate of 85.1% [14]. This study examined data from the Behavioral Risk Factor Surveillance Survey from 1995-2002 to determine whether or not disparities exist in the rate of unemployment for women with disabilities, compared to men with disabilities and women and men without disabilities. In addition, regression analysis looked at the how disability and gender predict the outcome of unemployment. Results showed that there has been essentially no change with regard to employment for any of these populations. In addition, disability and gender were found to be the strongest predictors of unemployment for women with disabilities. Possible explanations were discussed as to the reasons for the results and issues were presented for future research.     

Experiences of homelessness by people seeking asylum in Australia: A review of published and “grey” literature

This review examines the current published and “grey” literature relating to the experiences of homelessness by people seeking asylum within Australia. While many people seeking asylum have experienced homelessness during their flight, inadequate and unaffordable housing are a feature of the settlement experience for a large number of new arrivals to Australia, putting at risk their successful settlement. This study aims to explore the factors influencing access to stable housing. A systematic review of the academic literature and a search of the grey literature identified 11 articles and 20 documents related to homelessness and people seeking asylum in Australia. The academic literature included a variety of methods and approaches to the investigation of the experience of homelessness in Australia. The grey literature was more practice based, including evaluation and research reports, submissions, and newsletters. Findings suggest that experiences of homelessness are multifaceted and have a range of influences, including policy, financial stress, lack of access to education and employment, and a lack of affordable and suitable housing. Any solutions to address homelessness need to be similarly diverse. Discrimination from real estate agents and employers was also identified as a barrier to housing for many people seeking asylum, an issue that will need to be addressed at a systematic level. The lack of research published in the academic literature means that although there may be some programmes or solutions operating in the community, as demonstrated in the grey literature, this information is not being shared beyond organisation websites or subscriber lists, resulting in a lack of coherency or sharing of solutions. This review highlights the need for further attention to the factors influencing homelessness in asylum seekers, the experience of homelessness, and how these can be addressed by government and non-government led strategies and policy development.     

Graphical representation of survival curves in the presence of time-dependent categorical covariates with application to liver transplantation

Graphical representation of survival curves is often used to illustrate associations between exposures and time-to-event outcomes. However, when exposures are time-dependent, calculation of survival probabilities is not straightforward. Our aim was to develop a method to estimate time-dependent survival probabilities and represent them graphically. Cox models with time-dependent indicators to represent state changes were fitted, and survival probabilities were plotted using pre-specified times of state changes. Time-varying hazard ratios for the state change were also explored. The method was applied to data from the Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL). Survival curves showing a ‘split’ at a pre-specified time t allow for the qualitative comparison of survival probabilities between patients with similar baseline covariates who do and do not experience a state change at time t. Time since state change interactions can be visually represented to reflect changing hazard ratios over time. A2ALL study results showed differences in survival probabilities among those who did not receive a transplant, received a living donor transplant, and received a deceased donor transplant. These graphical representations of survival curves with time-dependent indicators improve upon previous methods and allow for clinically meaningful interpretation.     

A CANONICAL ANALYSIS OF MANAGERIAL-ENVIRONMENTAL RELATIONSHIPS: IMPLICATIONS FOR METHODOLOGY AND POLICY*

The present study uses canonical analysis to examine the contingency model of organizations in the nursing home field. Data on environmental pressures and managerial practices relevant to the decision-making process are analyzed. Results support the theory that managerial practices are contingent on environmental pressures. The implications of these results for health-care decision makers and for future assessments of managerial-environmental relationships are discussed.     

Empirical Prediction Intervals for County Population Forecasts

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.