Prevalence and determinants of the use of self-tests by members of the public: a mixed methods study

Background Self-tests can be used by members of the public to diagnose conditions without involving a doctor, nurse or other health professional. As technologies to design and manufacture diagnostic tests have developed, a range of self-tests have become available to the public to buy over-the-counter and via the Internet. This study aims to describe how many people have used self-tests and identify factors associated with their use. Methods A postal questionnaire will elicit basic information, including sociodemographic characteristics, and whether the person has used or would use specified self-tests. Consent will be sought to recontact people who want to participate further in the study, and interviews and focus groups will be used to develop hypotheses about factors associated with self-test use. These hypotheses will be tested in a case-control study. An in-depth questionnaire will be developed incorporating the identified factors. This will be sent to: people who have used a self-test (cases); people who have not used a self-test but would use one in the future (controls); and people who have not used and would not use a self-test (controls). Logistic regression analysis will be used to establish which factors are associated with self-test use. Discussion Self-tests do have potential benefits, for example privacy and convenience, but also potential harms, for example delay seeking treatment after a true negative result when the symptoms are actually due to another condition. It is anticipated that the outcomes from this study will include recommendations about how to improve the appropriate use of self-tests and existing health services, as well as information to prepare health professionals for patients who have used self-tests

Self-Association of an Activating Natural Killer Cell Receptor, KIR2DS1

As a major component of the innate immune system, natural killer cells are responsible for activating the cytolytic killing of certain pathogen-infected or tumor cells. The self-recognition of natural killer cells is achieved in part by the killer cell immunoglobulin-like receptors (KIRs) protein family. In the current study, using a suite of biophysical methods, we investigate the self-association of an activating KIR, KIR2DS1. This KIR is of particular interest because when in the presence of the HLA-Cw6 protein, KIR2DS1 becomes a major risk factor for psoriasis, an autoimmune chronic skin disease. Using circular dichroism spectroscopy, dynamic light scattering, and atomic force microscopy, we reveal that KIR2DS1 self-associates in a well-defined fashion. Our novel results on an activating KIR allow us to suggest a working model for the KIR2DS1- HLA class I molecular mechanism

Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

<p>Abstract</p> <p>Background</p> <p>Alefacept treatment is highly effective in a select group patients with moderate-to-severe psoriasis, and is an ideal candidate to develop systems to predict who will respond to therapy. A clinical trial of 22 patients with moderate to severe psoriasis treated with alefacept was conducted in 2002-2003, as a mechanism of action study. Patients were classified as responders or non-responders to alefacept based on histological criteria. Results of the original mechanism of action study have been published. Peripheral blood was collected at the start of this clinical trial, and a prior analysis demonstrated that gene expression in PBMCs differed between responders and non-responders, however, the analysis performed could not be used to predict response.</p> <p>Methods</p> <p>Microarray data from PBMCs of 16 of these patients was analyzed to generate a treatment response classifier. We used a discriminant analysis method that performs sample classification from gene expression data, via "nearest shrunken centroid method". Centroids are the average gene expression for each gene in each class divided by the within-class standard deviation for that gene.</p> <p>Results</p> <p>A disease response classifier using 23 genes was created to accurately predict response to alefacept (12.3% error rate). While the genes in this classifier should be considered as a group, some of the individual genes are of great interest, for example, cAMP response element modulator (CREM), v-MAF avian musculoaponeurotic fibrosarcoma oncogene family (MAFF), chloride intracellular channel protein 1 (CLIC1, also called NCC27), NLR family, pyrin domain-containing 1 (NLRP1), and CCL5 (chemokine, cc motif, ligand 5, also called regulated upon activation, normally T expressed, and presumably secreted/RANTES).</p> <p>Conclusions</p> <p>Although this study is small, and based on analysis of existing microarray data, we demonstrate that a treatment response classifier for alefacept can be created using gene expression of PBMCs in psoriasis. This preliminary study may provide a useful tool to predict response of psoriatic patients to alefacept.</p

How many educated workers for your economy? European targets, optimal public spending, and labor market impact

This paper studies optimal taxation schemes for education in a search- matching model where the labor market is divided between a high-skill and a low-skill sector. Two public policy targets - maximizing the total employment level and optimizing the social surplus - are studied according to three different public taxation strategies. We calibrate our model using evidence from thirteen European countries, and compare our results with the target from the Europe 2020 Agenda for achievement in higher education. We show that, with current labor market char- acteristics, the target set by governments seems compatible with the social surplus maximization objective for some countries, while being too high for other countries. For all countries, maximizing employment would imply higher educational spending than that required for the social surplus to reach its maximum.info:eu-repo/semantics/publishedVersio

Key factors influencing adoption of an innovation in primary health care: a qualitative study based on implementation theory

<p>Abstract</p> <p>Background</p> <p>Bridging the knowledge-to-practice gap in health care is an important issue that has gained interest in recent years. Implementing new methods, guidelines or tools into routine care, however, is a slow and unpredictable process, and the factors that play a role in the change process are not yet fully understood. There is a number of theories concerned with factors predicting successful implementation in various settings, however, this issue is insufficiently studied in primary health care (PHC). The objective of this article was to apply implementation theory to identify key factors influencing the adoption of an innovation being introduced in PHC in Sweden.</p> <p>Methods</p> <p>A qualitative study was carried out with staff at six PHC units in Sweden where a computer-based test for lifestyle intervention had been implemented. Two different implementation strategies, implicit or explicit, were used. Sixteen focus group interviews and two individual interviews were performed. In the analysis a theoretical framework based on studies of implementation in health service organizations, was applied to identify key factors influencing adoption.</p> <p>Results</p> <p>The theoretical framework proved to be relevant for studies in PHC. Adoption was positively influenced by positive expectations at the unit, perceptions of the innovation being compatible with existing routines and perceived advantages. An explicit implementation strategy and positive opinions on change and innovation were also associated with adoption. Organizational changes and staff shortages coinciding with implementation seemed to be obstacles for the adoption process.</p> <p>Conclusion</p> <p>When implementation theory obtained from studies in other areas was applied in PHC it proved to be relevant for this particular setting. Based on our results, factors to be taken into account in the planning of the implementation of a new tool in PHC should include assessment of staff expectations, assessment of the perceived need for the innovation to be implemented, and of its potential compatibility with existing routines. Regarding context, we suggest that implementation concurrent with other major organizational changes should be avoided. The choice of implementation strategy should be given thorough consideration.</p

Perceptions of primary healthcare professionals towards their role in type 2 diabetes mellitus patient education in Brazil

<p>Abstract</p> <p>Background</p> <p>The aim of the current study was to analyze the perceptions, knowledge, and practices of primary healthcare professionals in providing patient education to people with type 2 diabetes mellitus.</p> <p>Methods</p> <p>A total of 23 health professionals working in primary healthcare units in Belo Horizonte, Minas Gerais State, Brazil, participated in a focus group in order to discuss their patient education practices and the challenges for effective patient education in diabetes self-management.</p> <p>Results</p> <p>The results were categorized as follows: 1) lack of preparation and technical knowledge among the health professionals on some aspects of diabetes mellitus and the health professionals' patient education practices; 2) work conditions and organization; 3) issues related or attributed to the clientele themselves; and 4) diabetes care model.</p> <p>Conclusions</p> <p>This study highlights the importance of reorienting the patient education practices, health professionals' skills and work goals, and evaluation of the educational interventions, in order to establish strategies for health promotion and prevention and control of the disease.</p> <p>Descriptors</p> <p>Health Education; Prevention of Diabetes Mellitus; Primary Healthcare</p

Study protocol for the translating research in elder care (TREC): building context – an organizational monitoring program in long-term care project (project one)

<p>Abstract</p> <p>Background</p> <p>While there is a growing awareness of the importance of organizational context (or the work environment/setting) to successful knowledge translation, and successful knowledge translation to better patient, provider (staff), and system outcomes, little empirical evidence supports these assumptions. Further, little is known about the factors that enhance knowledge translation and better outcomes in residential long-term care facilities, where care has been shown to be suboptimal. The project described in this protocol is one of the two main projects of the larger five-year Translating Research in Elder Care (TREC) program.</p> <p>Aims</p> <p>The purpose of this project is to establish the magnitude of the effect of organizational context on knowledge translation, and subsequently on resident, staff (unregulated, regulated, and managerial) and system outcomes in long-term care facilities in the three Canadian Prairie Provinces (Alberta, Saskatchewan, Manitoba).</p> <p>Methods/Design</p> <p>This study protocol describes the details of a multi-level – including provinces, regions, facilities, units within facilities, and individuals who receive care (residents) or work (staff) in facilities – and longitudinal (five-year) research project. A stratified random sample of 36 residential long-term care facilities (30 urban and 6 rural) from the Canadian Prairie Provinces will comprise the sample. Caregivers and care managers within these facilities will be asked to complete the TREC survey – a suite of survey instruments designed to assess organizational context and related factors hypothesized to be important to successful knowledge translation and to achieving better resident, staff, and system outcomes. Facility and unit level data will be collected using standardized data collection forms, and resident outcomes using the Resident Assessment Instrument-Minimum Data Set version 2.0 instrument. A variety of analytic techniques will be employed including descriptive analyses, psychometric analyses, multi-level modeling, and mixed-method analyses.</p> <p>Discussion</p> <p>Three key challenging areas associated with conducting this project are discussed: sampling, participant recruitment, and sample retention; survey administration (with unregulated caregivers); and the provision of a stable set of study definitions to guide the project.</p

Structural diversity of biologically interesting datasets: a scaffold analysis approach

ABSTRACT:The recent public availability of the human metabolome and natural product datasets has revitalized "metabolite-likeness" and "natural product-likeness" as a drug design concept to design lead libraries targeting specific pathways. Many reports have analyzed the physicochemical property space of biologically important datasets, with only a few comprehensively characterizing the scaffold diversity in public datasets of biological interest. With large collections of high quality public data currently available, we carried out a comparative analysis of current day leads with other biologically relevant datasets.In this study, we note a two-fold enrichment of metabolite scaffolds in drug dataset (42%) as compared to currently used lead libraries (23%). We also note that only a small percentage (5%) of natural product scaffolds space is shared by the lead dataset. We have identified specific scaffolds that are present in metabolites and natural products, with close counterparts in the drugs, but are missing in the lead dataset. To determine the distribution of compounds in physicochemical property space we analyzed the molecular polar surface area, the molecular solubility, the number of rings and the number of rotatable bonds in addition to four well-known Lipinski properties. Here, we note that, with only few exceptions, most of the drugs follow Lipinski's rule. The average values of the molecular polar surface area and the molecular solubility in metabolites is the highest while the number of rings is the lowest. In addition, we note that natural products contain the maximum number of rings and the rotatable bonds than any other dataset under consideration.Currently used lead libraries make little use of the metabolites and natural products scaffold space. We believe that metabolites and natural products are recognized by at least one protein in the biosphere therefore, sampling the fragment and scaffold space of these compounds, along with the knowledge of distribution in physicochemical property space, can result in better lead libraries. Hence, we recommend the greater use of metabolites and natural products while designing lead libraries. Nevertheless, metabolites have a limited distribution in chemical space that limits the usage of metabolites in library design.14 page(s