Cardiac magnetic resonance findings predict increased resource utilization in elective coronary artery bypass grafting

Morbidity following CABG (coronary artery bypass grafting) is difficult to predict and leads to increased healthcare costs. We hypothesized that pre-operative CMR (cardiac magnetic resonance) findings would predict resource utilization in elective CABG. Over a 12-month period, patients requiring elective CABG were invited to undergo CMR 1 day prior to CABG. Gadolinium-enhanced CMR was performed using a trueFISP inversion recovery sequence on a 1.5 tesla scanner (Sonata; Siemens). Clinical data were collected prospectively. Admission costs were quantified based on standardized actual cost/day. Admission cost greater than the median was defined as 'increased'. Of 458 elective CABG cases, 45 (10%) underwent pre-operative CMR. Pre-operative characteristics [mean (S.D.) age, 64 (9) years, mortality (1%) and median (interquartile range) admission duration, 7 (6–8) days] were similar in patients who did or did not undergo CMR. In the patients undergoing CMR, eight (18%) and 11 (24%) patients had reduced LV (left ventricular) systolic function by CMR [LVEF (LV ejection fraction) <55%] and echocardiography respectively. LE (late enhancement) with gadolinium was detected in 17 (38%) patients. The average cost/day was $2723. The median (interquartile range) admission cost was$19059 ($10891–157917). CMR LVEF {OR (odds ratio), 0.93 [95% CI (confidence interval), 0.87–0.99]; P=0.03} and SV (stroke volume) index [OR 1.07 (95% CI, 1.00–1.14); P=0.02] predicted increased admission cost. CMR LVEF (P=0.08) and EuroScore tended to predict actual admission cost (P=0.09), but SV by CMR (P=0.16) and LV function by echocardiography (P=0.95) did not. In conclusion, in this exploratory investigation, pre-operative CMR findings predicted admission duration and increased admission cost in elective CABG surgery. The cost-effectiveness of CMR in risk stratification in elective CABG surgery merits prospective assessment

Observations of velocities, sand concentrations, and fluxes under velocity-asymmetric oscillatory flows

Peer reviewedPublisher PD

Behavioral Economic Measurement of Cigarette Demand: A Descriptive Review of Published Approaches to the Cigarette Purchase Task

The cigarette purchase task (CPT) is a behavioral economic method for assessing demand for cigarettes. Growing interest in behavioral correlates of tobacco use in clinical and general populations as well as empirical efforts to inform policy has seen an increase in published articles employing the CPT. Accordingly, an examination of the published methods and procedures for obtaining these behavioral economic metrics is timely. The purpose of this investigation was to provide a review of published approaches to using the CPT. We searched specific Boolean operators ([“behavioral economic” AND “purchase task”] OR [“demand” AND “cigarette”]) and identified 49 empirical articles published through the year 2018 that reported administering a CPT. Articles were coded for participant characteristics (e.g., sample size, population type, age), CPT task structure (e.g., price framing, number and sequence of prices; vignettes, contextual factors), and data analytic approach (e.g., method of generating indices of cigarette demand). Results of this review indicate no standard approach to administering the CPT and underscore the need for replicability of these behavioral economic measures for the purpose of guiding clinical and policy decisions

Attributing changes in the distribution of species abundance to weather variables using the example of British breeding birds

The BBS is undertaken by the British Trust for Ornithology (BTO) and jointly funded by the BTO, the Joint Nature Conservation Committee and the Royal Society for the Protection of Birds.1. Modelling spatio-temporal changes in species abundance and attributing those changes to potential drivers such as climate, is an important but difficult problem. The standard approach for incorporating climatic variables into such models is to include each weather variable as a single covariate whose effect is expressed through a low-order polynomial or smoother in an additive model. This, however, confounds the spatial and temporal effects of the covariates. 2. We developed a novel approach to distinguish between three types of change in any particular weather covariate. We decomposed the weather covariate into three new covariates by separating out temporal variation in weather (averaging over space), spatial variation in weather (averaging over years) and a space-time anomaly term (residual variation). These three covariates were each fitted separately in the models. We illustrate the approach using generalized additive models applied to count data for a selection of species from the UK’s Breeding Bird Survey, 1994-2013. The weather covariates considered were the mean temperatures during the preceding winter and temperatures and rainfall during the preceding breeding season. We compare models that include these covariates directly with models including decomposed components of the same covariates, considering both linear and smooth relationships. 3. The lowest QAIC values were always associated with a decomposed weather covariate model. Different relationships between counts and the three new covariates provided strong evidence that the effects of changes in covariate values depended on whether changes took place in space, in time, or in the space-time anomaly. These results promote caution in predicting species distribution and abundance in future climate, based on relationships that are largely determined by environmental variation over space. 4. Our methods estimate the effect of temporal changes in weather, while accounting for spatial effects of long-term climate, improving inference on overall and/or localized effects of climate change. With increasing availability of large-scale data sets, need is growing for appropriate analytical tools. The proposed decomposition of the weather variables represents an important advance by eliminating the confounding issue often inherent in analyses of large-scale data sets.PostprintPeer reviewe

Global prevalence of childhood cataract: a systematic review

Childhood cataract is an avoidable cause of visual disability worldwide and is a priority for VISION 2020: The Right to Sight. There is a paucity of information about the burden of cataract in children and the aim of this review is to assess the global prevalence of childhood cataract. The methodology for the review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a literature search for studies reporting estimates of prevalence or incidence of cataract among children (aged<18 years) at any global location using the Cochrane Library, Medline and Embase up to January 2015. No restrictions were imposed based on language or year of publication. Study quality was assessed using a critical appraisal tool designed for systematic reviews of prevalence. Twenty prevalence and four incidence studies of childhood cataract from five different geographical regions were included. The overall prevalence of childhood cataract and congenital cataract was in the range from 0.32 to 22.9/10000 children (median=1.03) and 0.63 to 9.74/10000 (median=1.71), respectively. The incidence ranged from 1.8 to 3.6/10000 per year. The prevalence of childhood cataract in low-income economies was found to be 0.42 to 2.05 compared with 0.63 to 13.6/10000 in high-income economies. There was no difference in the prevalence based on laterality or gender. This review highlights substantial gaps in the epidemiological knowledge of childhood cataract worldwide, particularly from low and lower middle-income economies. More studies are needed using standard definitions and case ascertainment methods with large enough sample sizes

Use of stochastic simulation to evaluate the reduction in methane emissions and improvement in reproductive efficiency from routine hormonal interventions in dairy herds

This study predicts the magnitude and between herd variation in changes of methane emissions and production efficiency associated with interventions to improve reproductive efficiency in dairy cows. Data for 10,000 herds of 200 cows were simulated. Probability of conception was predicted daily from the start of the study (parturition) for each cow up to day 300 of lactation. Four scenarios of differing first insemination management were simulated for each herd using the same theoretical cows: A baseline scenario based on breeding from observed oestrus only, synchronisation of oestrus for pre-set first insemination using 2 methods, and a regime using prostaglandin treatments followed by first insemination to observed oestrus. Cows that did not conceive to first insemination were re-inseminated following detection of oestrus. For cows that conceived, gestation length was 280 days with cessation of milking 60 days before calving. Those cows not pregnant after 300 days of lactation were culled and replaced by a heifer. Daily milk yield was calculated for 730 days from the start of the study for each cow. Change in mean reproductive and economic outputs were summarised for each herd following the 3 interventions. For each scenario, methane emissions were determined by daily forage dry matter intake, forage quality, and cow replacement risk. Linear regression was used to summarise relationships. In some circumstances improvement in reproductive efficiency using the programmes investigated was associated with reduced cost and methane emissions compared to reliance on detection of oestrus. Efficiency of oestrus detection and the time to commencement of breeding after calving influenced variability in changes in cost and methane emissions. For an average UK herd this was a saving of at least £50 per cow and a 3.6% reduction in methane emissions per L of milk when timing of first insemination was pre-set

Dietary glycaemic index, glycaemic load and endometrial and ovarian cancer risk: a systematic review and meta-analysis

Long-term consumption of a high glycaemic index (GI) or glycaemic load (GL) diet may lead to chronic hyperinsulinaemia, which is a potential risk factor for cancer. To date, many studies have examined the association between GI, GL and cancer risk, although results have been inconsistent, therefore our objective was to conduct a systematic review of the literature. Medline and Embase were systematically searched using terms for GI, GL and cancer to identify studies published before December 2007. Random effects meta-analyses were performed for endometrial cancer, combining maximally adjusted results that compared risk for those in the highest versus the lowest category of intake. Separate analysis examined risk by body mass index categories. Five studies examining GI and/or GL intake and endometrial cancer risk were identified. Pooled effect estimates for endometrial cancer showed an increased risk for high GL consumers (RR 1.20; 95% CI: 1.06–1.37), further elevated in obese women (RR 1.54; 95% CI: 1.18–2.03). No significant associations were observed for GI. Only two studies examined ovarian cancer and therefore no meta-analysis was performed, but results indicate positive associations for GL also. A high GL, but not a high GI, diet is positively associated with the risk of endometrial cancer, particularly among obese women

Citizen science for observing and understanding the Earth

Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges

Effect of genetic testing for risk of type 2 diabetes mellitus on health behaviors and outcomes: study rationale, development and design

<p>Abstract</p> <p>Background</p> <p>Type 2 diabetes is a prevalent chronic condition globally that results in extensive morbidity, decreased quality of life, and increased health services utilization. Lifestyle changes can prevent the development of diabetes, but require patient engagement. Genetic risk testing might represent a new tool to increase patients' motivation for lifestyle changes. Here we describe the rationale, development, and design of a randomized controlled trial (RCT) assessing the clinical and personal utility of incorporating type 2 diabetes genetic risk testing into comprehensive diabetes risk assessments performed in a primary care setting.</p> <p>Methods/Design</p> <p>Patients are recruited in the laboratory waiting areas of two primary care clinics and enrolled into one of three study arms. Those interested in genetic risk testing are randomized to receive <it>either </it>a standard risk assessment (SRA) for type 2 diabetes incorporating conventional risk factors plus upfront disclosure of the results of genetic risk testing ("SRA+G" arm), <it>or </it>the SRA alone ("SRA" arm). Participants not interested in genetic risk testing will not receive the test, but will receive SRA (forming a third, "no-test" arm). Risk counseling is provided by clinic staff (not study staff external to the clinic). Fasting plasma glucose, insulin levels, body mass index (BMI), and waist circumference are measured at baseline and 12 months, as are patients' self-reported behavioral and emotional responses to diabetes risk information. Primary outcomes are changes in insulin resistance and BMI after 12 months; secondary outcomes include changes in diet patterns, physical activity, waist circumference, and perceived risk of developing diabetes.</p> <p>Discussion</p> <p>The utility, feasibility, and efficacy of providing patients with genetic risk information for common chronic diseases in primary care remain unknown. The study described here will help to establish whether providing type 2 diabetes genetic risk information in a primary care setting can help improve patients' clinical outcomes, risk perceptions, and/or their engagement in healthy behavior change. In addition, study design features such as the use of existing clinic personnel for risk counseling could inform the future development and implementation of care models for the use of individual genetic risk information in primary care.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00849563">NCT00849563</a></p

Combined effect of CCND1 and COMT polymorphisms and increased breast cancer risk

<p>Abstract</p> <p>Background</p> <p>Estrogens are crucial tumorigenic hormones, which impact the cell growth and proliferation during breast cancer development. Estrogens are metabolized by a series of enzymes including COMT, which converts catechol estrogens into biologically non-hazardous methoxyestrogens. Several studies have also shown the relationship between estrogen and cell cycle progression through activation of CCND1 transcription.</p> <p>Methods</p> <p>In this study, we have investigated the independent and the combined effects of commonly occurring CCND1 (Pro241Pro, A870G) and COMT (Met108/158Val) polymorphisms to breast cancer risk in two independent Caucasian populations from Ontario (1228 breast cancer cases and 719 population controls) and Finland (728 breast cancer cases and 687 population controls). Both COMT and CCND1 polymorphisms have been previously shown to impact on the enzymatic activity of the coded proteins.</p> <p>Results</p> <p>Here, we have shown that the high enzymatic activity genotype of CCND1^High(AA) was associated with increased breast cancer risk in both the Ontario [OR: 1.3, 95%CI (1.0–1.69)] and the Finland sample [OR: 1.4, 95%CI (1.01–1.84)]. The heterozygous COMT^Medium(MetVal) and the high enzymatic activity of COMT^High(ValVal) genotype was also associated with breast cancer risk in Ontario cases, [OR: 1.3, 95%CI (1.07–1.68)] and [OR: 1.4, 95%CI (1.07–1.81)], respectively. However, there was neither a statistically significant association nor increased trend of breast cancer risk with COMT^High(ValVal) genotypes in the Finland cases [OR: 1.0, 95%CI (0.73–1.39)]. In the combined analysis, the higher activity alleles of the COMT and CCND1 is associated with increased breast cancer risk in both Ontario [OR: <b>2.22</b>, 95%CI (1.49–3.28)] and Finland [OR: <b>1.73</b>, 95%CI (1.08–2.78)] populations studied. The trend test was statistically significant in both the Ontario and Finland populations across the genotypes associated with increasing enzymatic activity.</p> <p>Conclusion</p> <p>Using two independent Caucasian populations, we have shown a stronger combined effect of the two commonly occurring CCND1 and COMT genotypes in the context of breast cancer predisposition.</p