Determinants of non attendance to mammography program in a region with high voluntary health insurance coverage

<p>Abstract</p> <p>Background</p> <p>High participation rates are needed to ensure that breast cancer screening programs effectively reduce mortality. We identified the determinants of non-participation in a public breast cancer screening program.</p> <p>Methods</p> <p>In this case-control study, 274 women aged 50 to 64 years included in a population-based mammography screening program were personally interviewed. Socio-demographic characteristics, health beliefs, health service utilization, insurance coverage, prior mammography and other preventive activities were examined.</p> <p>Results</p> <p>Of the 192 cases and 194 controls contacted, 101 and 173, respectively, were subsequently interviewed. Factors related to non-participation in the breast cancer screening program included higher education (odds ratio [OR] = 5.28; 95% confidence interval [CI95%] = 1.57–17.68), annual dental checks-ups (OR = 1.81; CI95%1.08–3.03), prior mammography at a private health center (OR = 7.27; CI95% 3.97–13.32), gynecologist recommendation of mammography (OR = 2.2; CI95%1.3–3.8), number of visits to a gynecologist (median visits by cases = 1.2, versus controls = 0.92, P = 0.001), and supplemental private insurance (OR = 5.62; CI95% = 3.28–9.6). Among women who had not received a prior mammogram or who had done so at a public center, perceived barriers were the main factors related to non-participation. Among women who had previously received mammograms at a private center, supplemental private health insurance also influenced non-participation. Benign breast symptoms increased the likelihood of participation.</p> <p>Conclusion</p> <p>Our data indicate that factors related to the type of insurance coverage (such as prior mammography at a private health center and supplemental private insurance) influenced non-participation in the screening program.</p

Comparative study of paediatric prescription drug utilization between the spanish and immigrant population

<p>Abstract</p> <p>Background</p> <p>The immigrant population has increased greatly in Spain in recent years to the point where immigrants made up 12% of the infant population in 2008. There is little information available on the profile of this group with regard to prescription drug utilization in universal public health care systems such as that operating in Spain. This work studies the overall and specific differences in prescription drug utilization between the immigrant and Spanish population.</p> <p>Methods</p> <p>Use was made of the Aragonese Health Service databases for 2006. The studied population comprises 159,908 children aged 0-14 years, 13.6% of whom are foreign nationals. Different utilization variables were calculated for each group. Prescription-drug consumption is measured in Defined Daily Doses (DDD) and DDD/1000 persons/day/(DID).</p> <p>Results</p> <p>A total of 833,223 prescriptions were studied. Utilization is lower for immigrant children than in Spanish children for both DID (66.27 v. 113.67) and average annual expense (€21.55 v. €41.14). Immigrant children consume fewer prescription drugs than Spanish children in all of the therapy groups, with the most prescribed (in DID) being: respiratory system, anti-infectives for systemic use, nervous system, sensory organs. Significant differences were observed in relation to the type of drugs and the geographical background of immigrants.</p> <p>Conclusion</p> <p>Prescription drug utilization is much greater in Spanish children than in immigrant children, particularly with reference to bronchodilators (montelukast and terbutaline) and attention-disorder hyperactivity drugs such as methylphenidate. There are important differences regarding drug type and depending on immigrants' geographical backgrounds that suggest there are social, cultural and access factors underlying these disparities.</p

Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging

Using medical images to evaluate disease severity and change over time is a routine and important task in clinical decision making. Grading systems are often used, but are unreliable as domain experts disagree on disease severity category thresholds. These discrete categories also do not reflect the underlying continuous spectrum of disease severity. To address these issues, we developed a convolutional Siamese neural network approach to evaluate disease severity at single time points and change between longitudinal patient visits on a continuous spectrum. We demonstrate this in two medical imaging domains: retinopathy of prematurity (ROP) in retinal photographs and osteoarthritis in knee radiographs. Our patient cohorts consist of 4861 images from 870 patients in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) cohort study and 10,012 images from 3021 patients in the Multicenter Osteoarthritis Study (MOST), both of which feature longitudinal imaging data. Multiple expert clinician raters ranked 100 retinal images and 100 knee radiographs from excluded test sets for severity of ROP and osteoarthritis, respectively. The Siamese neural network output for each image in comparison to a pool of normal reference images correlates with disease severity rank (ρ = 0.87 for ROP and ρ = 0.89 for osteoarthritis), both within and between the clinical grading categories. Thus, this output can represent the continuous spectrum of disease severity at any single time point. The difference in these outputs can be used to show change over time. Alternatively, paired images from the same patient at two time points can be directly compared using the Siamese neural network, resulting in an additional continuous measure of change between images. Importantly, our approach does not require manual localization of the pathology of interest and requires only a binary label for training (same versus different). The location of disease and site of change detected by the algorithm can be visualized using an occlusion sensitivity map-based approach. For a longitudinal binary change detection task, our Siamese neural networks achieve test set receiving operator characteristic area under the curves (AUCs) of up to 0.90 in evaluating ROP or knee osteoarthritis change, depending on the change detection strategy. The overall performance on this binary task is similar compared to a conventional convolutional deep-neural network trained for multi-class classification. Our results demonstrate that convolutional Siamese neural networks can be a powerful tool for evaluating the continuous spectrum of disease severity and change in medical imaging

Trastuzumab Produces Therapeutic Actions by Upregulating miR-26a and miR-30b in Breast Cancer Cells

OBJECTIVE: Trastuzumab has been used for the treatment of HER2-positive breast cancer (BC). However, a subset of BC patients exhibited resistance to trastuzumab therapy. Thus, clarifying the molecular mechanism of trastuzumab treatment will be beneficial to improve the treatment of HER2-positive BC patients. In this study, we identified trastuzumab-responsive microRNAs that are involved in the therapeutic effects of trastuzumab. METHODS AND RESULTS: RNA samples were obtained from HER2-positive (SKBR3 and BT474) and HER2-negetive (MCF7 and MDA-MB-231) cells with and without trastuzumab treatment for 6 days. Next, we conducted a microRNA profiling analysis using these samples to screen those microRNAs that were up- or down-regulated only in HER2-positive cells. This analysis identified miR-26a and miR-30b as trastuzumab-inducible microRNAs. Transfecting miR-26a and miR-30b induced cell growth suppression in the BC cells by 40% and 32%, respectively. A cell cycle analysis showed that these microRNAs induced G1 arrest in HER2-positive BC cells as trastuzumab did. An Annexin-V assay revealed that miR-26a but not miR-30b induced apoptosis in HER2-positive BC cells. Using the prediction algorithms for microRNA targets, we identified cyclin E2 (CCNE2) as a target gene of miR-30b. A luciferase-based reporter assay demonstrated that miR-30b post-transcriptionally reduced 27% (p = 0.005) of the gene expression by interacting with two binding sites in the 3'-UTR of CCNE2. CONCLUSION: In BC cells, trastuzumab modulated the expression of a subset of microRNAs, including miR-26a and miR-30b. The upregulation of miR-30b by trastuzumab may play a biological role in trastuzumab-induced cell growth inhibition by targeting CCNE2

Recent advances in systemic therapy: Advances in systemic therapy for HER2-positive metastatic breast cancer

Human epidermal growth factor receptor (HER)2 over-expression is associated with a shortened disease-free interval and poor survival. Although the addition of trastuzumab to chemotherapy in the first-line setting has improved response rates, progression-free survival, and overall survival, response rates declined when trastuzumab was used beyond the first-line setting because of multiple mechanisms of resistance. Studies have demonstrated the clinical utility of continuing trastuzumab beyond progression, and further trials to explore this concept are ongoing. New tyrosine kinase inhibitors, monoclonal antibodies, PTEN (phosphatase and tensin homolog) pathway regulators, HER2 antibody-drug conjugates, and inhibitors of heat shock protein-90 are being evaluated to determine whether they may have a role to play in treating trastuzumab-resistant metastatic breast cancer

Colorectal cancer health services research study protocol: the CCR-CARESS observational prospective cohort project

BACKGROUND: Colorectal cancers are one of the most common forms of malignancy worldwide. But two significant areas of research less studied deserve attention: health services use and development of patient stratification risk tools for these patients. METHODS:DESIGN: a prospective multicenter cohort study with a follow up period of up to 5 years after surgical intervention. Participant centers: 22 hospitals representing six autonomous communities of Spain. Participants/Study population: Patients diagnosed with colorectal cancer that have undergone surgical intervention and have consented to participate in the study between June 2010 and December 2012. Variables collected include pre-intervention background, sociodemographic parameters, hospital admission records, biological and clinical parameters, treatment information, and outcomes up to 5 years after surgical intervention. Patients completed the following questionnaires prior to surgery and in the follow up period: EuroQol-5D, EORTC QLQ-C30 (The European Organization for Research and Treatment of Cancer quality of life questionnaire) and QLQ-CR29 (module for colorectal cancer), the Duke Functional Social Support Questionnaire, the Hospital Anxiety and Depression Scale, and the Barthel Index. The main endpoints of the study are mortality, tumor recurrence, major complications, readmissions, and changes in health-related quality of life at 30 days and at 1, 2, 3 and 5 years after surgical intervention. STATISTICAL ANALYSIS: In relation to the different endpoints, predictive models will be used by means of multivariate logistic models, Cox or linear mixed-effects regression models. Simulation models for the prediction of discrete events in the long term will also be used, and an economic evaluation of different treatment strategies will be performed through the use of generalized linear models. DISCUSSION: The identification of potential risk factors for adverse events may help clinicians in the clinical decision making process. Also, the follow up by 5 years of this large cohort of patients may provide useful information to answer different health services research questions

Comparative efficacy of two primary care interventions to assist withdrawal from long term benzodiazepine use: A protocol for a clustered, randomized clinical trial

<p>Abstract</p> <p>Background</p> <p>Although benzodiazepines are effective, long-term use is not recommended because of potential adverse effects; the risks of tolerance and dependence; and an increased risk of hip fractures, motor vehicle accidents, and memory impairment. The estimated prevalence of long-term benzodiazepine use in the general population is about 2,2 to 2,6%, is higher in women and increases steadily with age. Interventions performed by General Practitioners may help patients to discontinue long-term benzodiazepine use. We have designed a trial to evaluate the effectiveness and safety of two brief general practitioner-provided interventions, based on gradual dose reduction, and will compare the effectiveness of these interventions with that of routine clinical practice.</p> <p>Methods/Design</p> <p>In a three-arm cluster randomized controlled trial, general practitioners will be randomly allocated to: a) a group in which the first patient visit will feature a structured interview, followed by visits every 2-3 weeks to the end of dose reduction; b) a group in which the first patient visit will feature a structured interview plus delivery of written instructions to self-reduce benzodiazepine dose, or c) routine care. Using a computerized pharmaceutical prescription database, 495 patients, aged 18-80 years, taking benzodiazepine for at least 6 months, will be recruited in primary care health districts of three regions of Spain (the Balearic Islands, Catalonia, and Valencia). The primary outcome will be benzodiazepine use at 12 months. The secondary outcomes will include measurements of anxiety and depression symptoms, benzodiazepine dependence, quality of sleep, and alcohol consumption.</p> <p>Discussion</p> <p>Although some interventions have been shown to be effective in reducing benzodiazepine consumption by long-term users, the clinical relevance of such interventions is limited by their complexity. This randomized trial will compare the effectiveness and safety of two complex stepped care interventions with that of routine care in a study with sufficient statistical power to detect clinically relevant differences.</p> <p>Trial Registration</p> <p>Current Controlled Trials: <a href="http://www.controlled-trials.com/ISRCTN13024375">ISRCTN13024375</a></p

New FTY720-docetaxel nanoparticle therapy overcomes FTY720-induced lymphopenia and inhibits metastatic breast tumour growth

Purpose: Combining molecular therapies with chemotherapy may offer an improved clinical outcome for chemoresistant tumours. Sphingosine-1-phosphate (S1P) receptor antagonist and sphingosine kinase 1 (SK1) inhibitor FTY720 (FTY) has promising anticancer properties, however, it causes systemic lymphopenia which impairs its use in cancer patients. In this study, we developed a nanoparticle (NP) combining docetaxel (DTX) and FTY for enhanced anticancer effect, targeted tumour delivery and reduced systemic toxicity. Methods: Docetaxel, FTY and glucosamine were covalently conjugated to poly(lactic-co-glycolic acid) (PLGA). NPs were characterised by dynamic light scattering and electron microscopy. The cellular uptake, cytotoxicity and in vivo antitumor efficacy of CNPs were evaluated. Results: We show for the first time that in triple negative breast cancer cells FTY provides chemosensitisation to DTX, allowing a four-fold reduction in the effective dose. We have encapsulated both drugs in PLGA complex NPs (CNPs), with narrow size distribution of ~ 100 nm and excellent cancer cell uptake providing sequential, sustained release of FTY and DTX. In triple negative breast cancer cells and mouse breast cancer models, CNPs had similar efficacy to systemic free therapies, but allowed an effective drug dose reduction. Application of CNPs has significantly reversed chemotherapy side effects such as weight loss, liver toxicity and, most notably, lymphopenia. Conclusions: We show for the first time the DTX chemosensitising effects of FTY in triple negative breast cancer. We further demonstrate that encapsulation of free drugs in CNPs can improve targeting, provide low off-target toxicity and most importantly reduce FTY-induced lymphopenia, offering potential therapeutic use of FTY in clinical cancer treatment

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing