Life expectancy at birth and all-cause mortality among people with personality disorder

Objective: It is well established that serious mental illness is associated with raised mortality, yet few studies have looked at the life expectancy of people with personality disorder (PD). This study aims to examine the life expectancy and relative mortality in people with PD within secondary mental health care. Methods: We set out to examine this using a large psychiatric case register in southeast London, UK. Mortality was obtained through national mortality tracing procedures. In a cohort of patients with a primary diagnosis of PD (n = 1836), standardised mortality ratios (SMRs) and life expectancies at birth were calculated, using general population mortality statistics as the comparator. Results: Life expectancy at birth was 63.3 years for women and 59.1 years for men with PD—18.7 years and 17.7 years shorter than females and males respectively in the general population in England and Wales. The SMR was 4.2 (95% CI: 3.03–5.64) overall; 5.0 (95% CI: 3.15–7.45) for females and 3.5 (95% CI: 2.17–5.47) for males. The highest SMRs were found in the younger age groups for both genders. Conclusion: People with PD using mental health services have a substantially reduced life expectancy, highlighting the significant public health burden of the disorder

Extracting antipsychotic polypharmacy data from electronic health records: developing and evaluating a novel process

Background Antipsychotic prescription information is commonly derived from structured fields in clinical health records. However, utilising diverse and comprehensive sources of information is especially important when investigating less frequent patterns of medication prescribing such as antipsychotic polypharmacy (APP). This study describes and evaluates a novel method of extracting APP data from both structured and free-text fields in electronic health records (EHRs), and its use for research purposes. Methods Using anonymised EHRs, we identified a cohort of patients with serious mental illness (SMI) who were treated in South London and Maudsley NHS Foundation Trust mental health care services between 1 January and 30 June 2012. Information about antipsychotic co-prescribing was extracted using a combination of natural language processing and a bespoke algorithm. The validity of the data derived through this process was assessed against a manually coded gold standard to establish precision and recall. Lastly, we estimated the prevalence and patterns of antipsychotic polypharmacy. Results Individual instances of antipsychotic prescribing were detected with high precision (0.94 to 0.97) and moderate recall (0.57-0.77). We detected baseline APP (two or more antipsychotics prescribed in any 6-week window) with 0.92 precision and 0.74 recall and long-term APP (antipsychotic co-prescribing for 6 months) with 0.94 precision and 0.60 recall. Of the 7,201 SMI patients receiving active care during the observation period, 338 (4.7 %; 95 % CI 4.2-5.2) were identified as receiving long-term APP. Two second generation antipsychotics (64.8 %); and first -second generation antipsychotics were most commonly co-prescribed (32.5 %). Conclusions These results suggest that this is a potentially practical tool for identifying polypharmacy from mental health EHRs on a large scale. Furthermore, extracted data can be used to allow researchers to characterize patterns of polypharmacy over time including different drug combinations, trends in polypharmacy prescribing, predictors of polypharmacy prescribing and the impact of polypharmacy on patient outcomes

Reducing day-level emotional exhaustion: The complementary role of high involvement work systems and engaging leadership

High involvement work systems (HIWS) have been found to be improve employee well-being. The underlying processes through which HIWS influence employee well-being and the conditions under which these practices work are not fully understood. This study draws on job demands-resources theory to address this gap by theorising two novel mediators, that is, work pressure and bonding social capital, to explain how HIWS influence emotional exhaustion. We further proposed that engaging leadership as a proxy of line manager implementation of HIWS would strengthen these relationships. An integrated model is presented on how, why, and when HIWS influence employee well-being. Using data collected from 97 employees in a pharmaceutical company via a general survey and then a diary survey for 5 working days, this study found that HIWS alleviated day-level emotional exhaustion through their experience of higher day-level bonding social capital and lower day-level work pressure and these relationships were stronger under high level of engaging leadership

Superconductors with Magnetic Impurities: Instantons and Sub-gap States

When subject to a weak magnetic impurity potential, the order parameter and quasi-particle energy gap of a bulk singlet superconductor are suppressed. According to the conventional mean-field theory of Abrikosov and Gor'kov, the integrity of the energy gap is maintained up to a critical concentration of magnetic impurities. In this paper, a field theoretic approach is developed to critically analyze the validity of the mean field theory. Using the supersymmetry technique we find a spatially homogeneous saddle-point that reproduces the Abrikosov-Gor'kov theory, and identify instanton contributions to the density of states that render the quasi-particle energy gap soft at any non-zero magnetic impurity concentration. The sub-gap states are associated with supersymmetry broken field configurations of the action. An analysis of fluctuations around these configurations shows how the underlying supersymmetry of the action is restored by zero modes. An estimate of the density of states is given for all dimensionalities. To illustrate the universality of the present scheme we apply the same method to study `gap fluctuations' in a normal quantum dot coupled to a superconducting terminal. Using the same instanton approach, we recover the universal result recently proposed by Vavilov et al. Finally, we emphasize the universality of the present scheme for the description of gap fluctuations in d-dimensional superconducting/normal structures.Comment: 18 pages, 9 eps figure

Probing the high momentum component of the deuteron at high Q^2

The d(e,e'p) cross section at a momentum transfer of 3.5 (GeV/c)^2 was measured over a kinematical range that made it possible to study this reaction for a set of fixed missing momenta as a function of the neutron recoil angle theta_nq and to extract missing momentum distributions for fixed values of theta_nq up to 0.55 GeV/c. In the region of 35 (deg) <= theta_nq <= 45 (deg) recent calculations, which predict that final state interactions are small, agree reasonably well with the experimental data. Therefore these experimental reduced cross sections provide direct access to the high momentum component of the deuteron momentum distribution in exclusive deuteron electro-disintegration.Comment: 5 pages, 2 figure

Scaling Tests of the Cross Section for Deeply Virtual Compton Scattering

We present the first measurements of the \vec{e}p->epg cross section in the deeply virtual Compton scattering (DVCS) regime and the valence quark region. The Q^2 dependence (from 1.5 to 2.3 GeV^2) of the helicity-dependent cross section indicates the twist-2 dominance of DVCS, proving that generalized parton distributions (GPDs) are accessible to experiment at moderate Q^2. The helicity-independent cross section is also measured at Q^2=2.3 GeV^2. We present the first model-independent measurement of linear combinations of GPDs and GPD integrals up to the twist-3 approximation.Comment: 5 pages, 4 figures, 2 tables. Text shortened for publication. References added. One figure remove

Exclusive Neutral Pion Electroproduction in the Deeply Virtual Regime

We present measurements of the ep->ep pi^0 cross section extracted at two values of four-momentum transfer Q^2=1.9 GeV^2 and Q^2=2.3 GeV^2 at Jefferson Lab Hall A. The kinematic range allows to study the evolution of the extracted hadronic tensor as a function of Q^2 and W. Results will be confronted with Regge inspired calculations and GPD predictions. An intepretation of our data within the framework of semi-inclusive deep inelastic scattering has also been attempted

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey