Barriers to participation in mental health research: are there specific gender, ethnicity and age related barriers?

<p>Abstract</p> <p>Background</p> <p>It is well established that the incidence, prevalence and presentation of mental disorders differ by gender, ethnicity and age, and there is evidence that there is also differential representation in mental health research by these characteristics. The aim of this paper is to a) review the current literature on the nature of barriers to participation in mental health research, with particular reference to gender, age and ethnicity; b) review the evidence on the effectiveness of strategies used to overcome these barriers.</p> <p>Method</p> <p>Studies published up to December 2008 were identified using MEDLINE, PsycINFO and EMBASE using relevant mesh headings and keywords.</p> <p>Results</p> <p>Forty-nine papers were identified. There was evidence of a wide range of barriers including transportation difficulties, distrust and suspicion of researchers, and the stigma attached to mental illness. Strategies to overcome these barriers included the use of bilingual staff, assistance with travel, avoiding the use of stigmatising language in marketing material and a focus on education about the disorder under investigation. There were very few evaluations of such strategies, but there was evidence that ethnically matching recruiters to potential participants did not improve recruitment rates. Educational strategies were helpful and increased recruitment.</p> <p>Conclusion</p> <p>Mental health researchers should consider including caregivers in recruitment procedures where possible, provide clear descriptions of study aims and describe the representativeness of their sample when reporting study results. Studies that systematically investigate strategies to overcome barriers to recruitment are needed.</p

Systemic Biomarkers of Neutrophilic Inflammation, Tissue Injury and Repair in COPD Patients with Differing Levels of Disease Severity

The identification and validation of biomarkers to support the assessment of novel therapeutics for COPD continues to be an important area of research. The aim of the current study was to identify systemic protein biomarkers correlated with measures of COPD severity, as well as specific protein signatures associated with comorbidities such as metabolic syndrome. 142 protein analytes were measured in serum of 140 patients with stable COPD, 15 smokers without COPD and 30 non-smoking controls. Seven analytes (sRAGE, EN-RAGE, NGAL, Fibrinogen, MPO, TGF-α and HB-EGF) showed significant differences between severe/very severe COPD, mild/moderate COPD, smoking and non-smoking control groups. Within the COPD subjects, univariate and multivariate analyses identified analytes significantly associated with FEV1, FEV1/FVC and DLCO. Most notably, a set of 5 analytes (HB-EGF, Fibrinogen, MCP-4, sRAGE and Sortilin) predicted 21% of the variability in DLCO values. To determine common functions/pathways, analytes were clustered in a correlation network by similarity of expression profile. While analytes related to neutrophil function (EN-RAGE, NGAL, MPO) grouped together to form a cluster associated with FEV1 related parameters, analytes related to the EGFR pathway (HB-EGF, TGF-α) formed another cluster associated with both DLCO and FEV1 related parameters. Associations of Fibrinogen with DLCO and MPO with FEV1/FVC were stronger in patients without metabolic syndrome (r  =  −0.52, p  = 0.005 and r  =  −0.61, p  = 0.023, respectively) compared to patients with coexisting metabolic syndrome (r  =  −0.25, p  = 0.47 and r  =  −0.15, p  = 0.96, respectively), and may be driving overall associations in the general cohort. In summary, our study has identified known and novel serum protein biomarkers and has demonstrated specific associations with COPD disease severity, FEV1, FEV1/FVC and DLCO. These data highlight systemic inflammatory pathways, neutrophil activation and epithelial tissue injury/repair processes as key pathways associated with COPD

Biosensing with Virus Electrode Hybrids

Virus electrodes address two major challenges associated with biosensing. First, the surface of the viruses can be readily tailored for specific, high affinity binding to targeted biomarkers. Second, the viruses are entrapped in a conducting polymer for electrical resistance-based, quantitative measurement of biomarker concentration. To further enhance device sensitivity, two different ligands can be attached to the virus surface, and increase the apparent affinity for the biomarker. In the example presented here, the two ligands bind to the analyte in a bidentate binding mode with chelate-based avidity effect, and result in an 100 pM experimentally observed limit of detection for the cancer biomarker prostate-specific membrane antigen. The approach does not require enzymatic amplification, and allows reagent-free, real-time measurements. This article presents general protocols for the development of such biosensors with modified viruses for the enhanced detection of arbitrary target proteins

Is Physician Self-disclosure Related to Patient Evaluation of Office Visits?

CONTEXT: Physician self-disclosure has been viewed either positively or negatively, but little is known about how patients respond to physician self-disclosure. OBJECTIVE: To explore the possible relationship of physician self-disclosure to patient satisfaction. DESIGN: Routine office visits were audiotaped and coded for physician self-disclosure using the Roter Interaction Analysis System (RIAS). Physician self-disclosure was defined as a statement describing the physician's personal experience that has medical and/or emotional relevance for the patient. We stratified our analysis by physician specialty and compared patient satisfaction following visits in which physician self-disclosure did or did not occur. PARTICIPANTS: Patients (N = 1,265) who visited 59 primary care physicians and 65 surgeons. MAIN OUTCOME MEASURE: Patient satisfaction following the visit. RESULTS: Physician self-disclosure occurred in 17% (102/589) of primary care visits and 14% (93/676) of surgical visits. Following visits in which a primary care physician self-disclosed, fewer patients reported feelings of warmth/friendliness (37% vs 52%; P = .008) and reassurance/comfort (42% vs 55%; P = .027), and fewer reported being very satisfied with the visit (74% vs 83%; P = .031). Following visits in which a surgeon self-disclosed, more patients reported feelings of warmth/friendliness (60% vs 45%; P = .009) and reassurance/comfort (59% vs 47%; P = .044), and more reported being very satisfied with the visit (88% vs 75%; P = .007). After adjustment for patient characteristics, length of the visit, and other physician communication behaviors, primary care patients remained less satisfied (adjusted odds ratio [AOR], 0.45; 95% confidence interval [CI], 0.24 to 0.81) and surgical patients more satisfied (AOR, 2.22; 95% CI, 1.12 to 4.50) after visits in which the physician self-disclosed. CONCLUSIONS: Physician self-disclosure is significantly associated with higher patient satisfaction ratings for surgical visits and lower patient satisfaction ratings for primary care visits. Further study is needed to explore these intriguing findings and to define the circumstances under which physician self-disclosure is either well or poorly received

The Cancer Genomics Hub (CGHub): overcoming cancer through the power of torrential data.

The Cancer Genomics Hub (CGHub) is the online repository of the sequencing programs of the National Cancer Institute (NCI), including The Cancer Genomics Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects, with data from 25 different types of cancer. The CGHub currently contains &gt;1.4 PB of data, has grown at an average rate of 50 TB a month and serves &gt;100 TB per week. The architecture of CGHub is designed to support bulk searching and downloading through a Web-accessible application programming interface, enforce patient genome confidentiality in data storage and transmission and optimize for efficiency in access and transfer. In this article, we describe the design of these three components, present performance results for our transfer protocol, GeneTorrent, and finally report on the growth of the system in terms of data stored and transferred, including estimated limits on the current architecture. Our experienced-based estimates suggest that centralizing storage and computational resources is more efficient than wide distribution across many satellite labs. Database URL: https://cghub.ucsc.edu