Gender shift in realisation of preferred type of gp practice: longitudinal survey over the last 25 years

<p>Abstract</p> <p>Background</p> <p>An increasing number of newly trained Dutch GPs prefer to work in a group practice and as a non-principal rather than in a single-handed practice. In view of the greater number of female doctors, changing practice preferences, and discussions on future workforce problems, the question is whether male and female GPs were able to realise their initial preferences in the past and will be able to do so in the future.</p> <p>Methods</p> <p>We have conducted longitudinal cohort study of all GPs in the Netherlands seeking a practice between 1980 and 2004. The Netherlands Institute of Health Services Research (NIVEL) in Utrecht collected the data used in this study by means of a postal questionnaire. The overall mean response rate was 94%.</p> <p>Results</p> <p>Over the past 20 years, an increasing proportion of GPs, both male and female, were able to achieve their preference for working in a group practice and/or in a non-principal position. Relatively more women than men have settled in group practices, and more men than women in single-handed practices; however, the practice preference of men and women is beginning to converge. Dropout was highest among the GPs without any specific practice preference.</p> <p>Conclusion</p> <p>The overwhelming preference of male and female GPs for working in group practices is apparently being met by the number of positions (principal or non-principal) available in group practices. The preference of male and female GPs regarding the type of practice and job conditions is expected to converge further in the near future.</p

Use of mental health services among disaster survivors: predisposing factors

<p>Abstract</p> <p>Background</p> <p>Given the high prevalence of mental health problems after disasters it is important to study health services utilization. This study examines predictors for mental health services (MHS) utilization among survivors of a man-made disaster in the Netherlands (May 2000).</p> <p>Methods</p> <p>Electronic records of survivors (n = 339; over 18 years and older) registered in a mental health service (MHS) were linked with general practice based electronic medical records (EMRs) of survivors and data obtained in surveys. EMR data were available from 16 months pre-disaster until 3 years post-disaster. Symptoms and diagnoses in the EMRs were coded according to the International Classification of Primary Care (ICPC). Surveys were carried out 2–3 weeks and 18 months post-disaster, and included validated questionnaires on psychological distress, post-traumatic stress reactions and social functioning. Demographic and disaster-related variables were available. Predisposing factors for MHS utilization 0–18 months and 18–36 months post-disaster were examined using multiple logistic regression models.</p> <p>Results</p> <p>In multiple logistic models, adjusting for demographic and disaster related variables, MHS utilization was predicted by demographic variables (young age, immigrant, public health insurance, unemployment), disaster-related exposure (relocation and injuries), self-reported psychological problems and pre- and post-disaster physician diagnosed health problems (chronic diseases, musculoskeletal problems). After controlling for all health variables, disaster intrusions and avoidance reactions (OR:2.86; CI:1.48–5.53), hostility (OR:2.04; CI:1.28–3.25), pre-disaster chronic diseases (OR:1.82; CI:1.25–2.65), injuries as a result of the disaster (OR:1.80;CI:1.13–2.86), social functioning problems (OR:1.61;CI:1.05–2.44) and younger age (OR:0.98;CI:0.96–0.99) predicted MHS utilization within 18 months post-disaster. Furthermore, disaster intrusions and avoidance reactions (OR:2.29;CI:1.04–5.07) and hostility (OR:3.77;CI:1.51–9.40) predicted MHS utilization following 18 months post-disaster.</p> <p>Conclusion</p> <p>This study showed that several demographic and disaster-related variables and self-reported and physician diagnosed health problems predicted post-disaster MHS-use. The most important factors to predict post-disaster MHS utilization were disaster intrusions and avoidance reactions and symptoms of hostility (which can be identified as symptoms of PTSD) and pre-disaster chronic diseases.</p

Simian virus 40 inhibits differentiation and maturation of rhesus macaque DC-SIGN+-dendritic cells

Dendritic cells (DC) are the initiators and modulators of the immune responses. Some species of pathogenic microorganisms have developed immune evasion strategies by controlling antigen presentation function of DC. Simian virus 40 (SV40) is a DNA tumor virus of rhesus monkey origin. It can induce cell transformation and tumorigenesis in many vertebrate species, but often causes no visible effects and persists as a latent infection in rhesus monkeys under natural conditions. To investigate the interaction between SV40 and rhesus monkey DC, rhesus monkey peripheral blood monocyte-derived DC were induced using recombinant human Interleukin-4 (rhIL-4) and infective SV40, the phenotype and function of DC-specific intracellular adhesion molecule-3 grabbing nonintegrin (DC-SIGN)+ DC were analyzed by flow cytometry (FCM) and mixed lymphocyte reaction (MLR). Results showed that SV40 can down-regulate the expression of CD83 and CD86 on DC and impair DC-induced activation of T cell proliferation. These findings suggest that SV40 might also cause immune suppression by influencing differentiation and maturation of DC

Protocol for the development of a CONSORT extension for RCTs using cohorts and routinely collected health data.

Background: Randomized controlled trials (RCTs) are often complex and expensive to perform. Less than one third achieve planned recruitment targets, follow-up can be labor-intensive, and many have limited real-world generalizability. Designs for RCTs conducted using cohorts and routinely collected health data, including registries, electronic health records, and administrative databases, have been proposed to address these challenges and are being rapidly adopted. These designs, however, are relatively recent innovations, and published RCT reports often do not describe important aspects of their methodology in a standardized way. Our objective is to extend the Consolidated Standards of Reporting Trials (CONSORT) statement with a consensus-driven reporting guideline for RCTs using cohorts and routinely collected health data. Methods: The development of this CONSORT extension will consist of five phases. Phase 1 (completed) consisted of the project launch, including fundraising, the establishment of a research team, and development of a conceptual framework. In phase 2, a systematic review will be performed to identify publications (1) that describe methods or reporting considerations for RCTs conducted using cohorts and routinely collected health data or (2) that are protocols or report results from such RCTs. An initial "long list" of possible modifications to CONSORT checklist items and possible new items for the reporting guideline will be generated based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statements. Additional possible modifications and new items will be identified based on the results of the systematic review. Phase 3 will consist of a three-round Delphi exercise with methods and content experts to evaluate the "long list" and generate a "short list" of key items. In phase 4, these items will serve as the basis for an in-person consensus meeting to finalize a core set of items to be included in the reporting guideline and checklist. Phase 5 will involve drafting the checklist and elaboration-explanation documents, and dissemination and implementation of the guideline. Discussion: Development of this CONSORT extension will contribute to more transparent reporting of RCTs conducted using cohorts and routinely collected health data

Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities

Automatic segmentation of medical images is an important task for many clinical applications. In practice,a wide range of anatomical structures are visualised using different imaging modalities. In this paper,we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain images,the pectoral muscle in MR breast images,and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality,the visualised anatomical structures,and the tissue classes. For each of the three tasks (brain MRI,breast MRI and cardiac CTA),this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task,demonstrating the high capacity of CNN architectures. Hence,a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training