Symptoms of Mental Health Problems: Children’s and Adolescents’ Understandings and Implications for Gender Differences in Help Seeking

Amidst concerns that young people’s mental health is deteriorating, it is important to explore their understandings of symptoms of mental health problems and beliefs around help seeking. Drawing on focus group data from Scottish school pupils, we demonstrate how they understood symptoms of mental health problems and how their characterisations of these symptoms as ‘rare’ and ‘weird’ informed participants’ perceptions that peers, teachers and parents would respond to disclosure in stigmatising ways. Consequently, participants suggested that they would delay or avoid disclosing symptoms of mental health problems. We highlight subtle gender and age differences and outline implications for policy and practice.     

Large scale communication networks—Design and analysis

The paper discusses the problem of design and analysis of large scale communication systems. An iterative process, composed of a minimum cost network design algorithm and a network performance algorithm, is presented for the solution of these problems. Computational considerations, using these algorithms, are discussed.     

Making predictions from complex longitudinal data, with application to planning monitoring intervals in a national screening programme

When biological or physiological variables change over time, we are often interested in making predictions either of future measurements or of the time taken to reach some threshold value. On the basis of longitudinal data for multiple individuals, we develop Bayesian hierarchical models for making these predictions together with their associated uncertainty. Particular aspects addressed, which include some novel components, are handling curvature in individuals' trends over time, making predictions for both underlying and measured levels, making predictions from a single baseline measurement, making predictions from a series of measurements, allowing flexibility in the error and random-effects distributions, and including covariates. In the context of data on the expansion of abdominal aortic aneurysms over time, where reaching a certain threshold leads to referral for surgery, we discuss the practical application of these models to the planning of monitoring intervals in a national screening programme. Prediction of the time to reach a threshold was too imprecise to be practically useful, and we focus instead on limiting the probability of exceeding the threshold after given time intervals. Although more complex models can be shown to fit the data better, we find that relatively simple models seem to be adequate for planning monitoring intervals.     

Application of a predictive distribution formula to Bayesian computation for incomplete data models

We consider exact and approximate Bayesian computation in the presence of latent variables or missing data. Specifically we explore the application of a posterior predictive distribution formula derived in Sweeting And Kharroubi (2003), which is a particular form of Laplace approximation, both as an importance function and a proposal distribution. We show that this formula provides a stable importance function for use within poor man’s data augmentation schemes and that it can also be used as a proposal distribution within a Metropolis-Hastings algorithm for models that are not analytically tractable. We illustrate both uses in the case of a censored regression model and a normal hierarchical model, with both normal and Student t distributed random effects. Although the predictive distribution formula is motivated by regular asymptotic theory, it is not necessary that the likelihood has a closed form or that it possesses a local maximum.     

Assessing the effectiveness of strategic planning

This paper describes a method for assessing the effectiveness of strategic planning systems in terms of the results actually observed subsequent to planning activity. The paper explains in particular how, in making the assessment, one might cope with the fact of environmental change subsequent to the setting of planned goals. This ends oriented approach to measuring effectiveness is shown to be complementary to process oriented approaches and is illustrated by an example from the coal industry.     

Improving homeless persons' utilisation of primary care: lessons to be learned from an outreach programme in The Netherlands

Elissen AMJ, Van Raak AJA, Derckx EWCC, Vrijhoef HJM. Improving homeless persons' utilisation of primary care: lessons to be learned from an outreach programme in The Netherlands Faced with rising homelessness, countries around the world are in need of innovative approaches to caring for those without shelter, who, more often than not, suffer from severe health problems. We conducted a case study of an innovative Dutch Primary Care for the Homeless (PCH) programme to gain insight into clients' demographic characteristics, health problems and service use, and to develop an explanation for its success in increasing the latter. Our analyses are based on a combination of quantitative and qualitative data. The results of the study suggest that the success of the PCH programme can be explained by the providers' pragmatism and will to adapt their mode of care provision to the behavioural patterns and needs of their homeless clients.     

Selecting the precision parameter prior in Dirichlet process mixture models

We consider Dirichlet process mixture models in which the observed clusters in any particular dataset are not viewed as belonging to a finite set of possible clusters but rather as representatives of a latent structure in which objects belong to one of a potentially infinite number of clusters. As more information is revealed the number of inferred clusters is allowed to grow. The precision parameter of the Dirichlet process is a crucial parameter that controls the number of clusters. We develop a framework for the specification of the hyperparameters associated with the prior for the precision parameter that can be used both in the presence or absence of subjective prior information about the level of clustering. Our approach is illustrated in an analysis of clustering brands at the magazine Which?. The results are compared with the approach of Dorazio (2009) via a simulation study.     

On the construction of Bayes–confidence regions

We obtain approximate Bayes–confidence intervals for a scalar parameter based on directed likelihood. The posterior probabilities of these intervals agree with their unconditional coverage probabilities to fourth order, and with their conditional coverage probabilities to third order. These intervals are constructed for arbitrary smooth prior distributions. A key feature of the construction is that log-likelihood derivatives beyond second order are not required, unlike the asymptotic expansions of Severini.     

Misspecification of at‐risk periods and distributional assumptions in estimating COPD exacerbation rates: The resultant bias in treatment effect estimation

In trials comparing the rate of chronic obstructive pulmonary disease exacerbation between treatment arms, the rate is typically calculated on the basis of the whole of each patient's follow‐up period. However, the true time a patient is at risk should exclude periods in which an exacerbation episode is occurring, because a patient cannot be at risk of another exacerbation episode until recovered. We used data from two chronic obstructive pulmonary disease randomized controlled trials and compared treatment effect estimates and confidence intervals when using two different definitions of the at‐risk period. Using a simulation study we examined the bias in the estimated treatment effect and the coverage of the confidence interval, using these two definitions of the at‐risk period. We investigated how the sample size required for a given power changes on the basis of the definition of at‐risk period used. Our results showed that treatment efficacy is underestimated when the at‐risk period does not take account of exacerbation duration, and the power to detect a statistically significant result is slightly diminished. Correspondingly, using the correct at‐risk period, some modest savings in required sample size can be achieved. Using the proposed at‐risk period that excludes recovery times requires formal definitions of the beginning and end of an exacerbation episode, and we recommend these be always predefined in a trial protocol.