Estimating Small Area Income Deprivation: An Iterative Proportional Fitting Approach

Small area estimation and in particular the estimation of small area income deprivation has potential value in the development of new or alternative components of multiple deprivation indices. These new approaches enable the development of income distribution threshold based as opposed to benefit count based measures of income deprivation and so enable the alignment of regional and national measures such as the Households Below Average Income with small area measures. This paper briefly reviews a number of approaches to small area estimation before describing in some detail an iterative proportional fitting based spatial microsimulation approach. This approach is then applied to the estimation of small area HBAI rates at the small area level in Wales in 2003-5. The paper discusses the results of this approach, contrasts them with contemporary ‘official’ income deprivation measures for the same areas and describes a range of ways to assess the robustness of the results

Assessing the impact of a research funder’s recommendation to consider core outcome sets

Background Core outcome sets (COS) have the potential to reduce waste in research by improving the consistency of outcomes measured in trials of the same health condition. However, this reduction in waste will only be realised through the uptake of COS by clinical trialists. Without uptake, the continued development of COS that are not implemented may add to waste in research. Funders of clinical trials have the potential to have an impact on COS uptake by recommending their use to those applying for funding. The aim of our study was to assess the extent to which applicants followed the National Institute for Health Research Health Technology Assessment (NIHR HTA) programme’s recommendation to search for a COS to include in their clinical trial. Methods and findings We examined the outcomes section and detailed project descriptions of all 95 researcher-led primary research applications submitted to the NIHR HTA between January 2012, when the recommendation to search for a COS was included in the guidance for applicants, and December 2015 for evidence that a search for a COS had taken place and rationale for outcome choice in the absence of COS. A survey of applicants was conducted to further explore their use of COS and choice of outcomes with a response rate of 49%. Nine out of 95 applicants (10%) stated in their application that they had searched the COMET (Core Outcome Measures for Effectiveness Trials) Initiative database for a COS and another nine referred to searching for a COS using another method, e.g. a review of the literature. Of the 77 (81%) applicants that did not mention COMET or COS in their application, eight stated in the survey that they had searched the COMET database and ten carried out a search using another method. Some applicants who did not search for a COS gave reasons for their choice of outcomes including taking advice from patients and the public and choosing outcomes used in previous trials. Conclusion A funding body can have an impact on COS uptake by encouraging trialists to search for a COS. Funders could take further steps by putting processes in place to prompt applicants to be explicit about searching for COS in their application and notifying the funding board if a search has not taken place. The sources of information used by trialists to make decisions about outcomes in the absence of COS may suggest methods of dissemination for COS

Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA

This is the final version. Available on open access from the International Society for Bayesian Analysis via the DOI in this recordExposure to air pollution in the form of fine particulate matter (PM2.5) is known to cause diseases and cancers. Consequently, the public are increasingly seeking health warnings associated with levels of PM2.5 using mobile phone applications and websites. Often, these existing platforms provide one-size-fits-all guidance, not incorporating user specific personal preferences. This study demonstrates an innovative approach using Bayesian methods to support personalised decision making for air quality. We present a novel hierarchical spatio-temporal model for city air quality that includes buildings as barriers and captures covariate information. Detailed high resolution PM2.5 data from a single mobile air quality sensor is used to train the model, which is fit using R-INLA to facilitate computation at operational timescales. A method for eliciting multi-attribute utility for individual journeys within a city is then given, providing the user with Bayes-optimal journey decision support. As a proof-of-concept, the methodology is demonstrated using a set of journeys and air quality data collected in Brisbane city centre, Australia

Impulsivity is Associated with Increased Metabolism in the Fronto-Insular Network in Parkinson’s Disease

Front. Behav. Neurosci. 9:317. doi: 10.3389/fnbeh.2015.00317 Various neuroimaging studies demonstrated that the fronto-insular network is implicated in impulsive behavior. We compared glucose metabolism (as a proxy measure of neural activity) among 24 patients with Parkinson’s disease (PD) who presented with low or high levels of impulsivity based on the Barratt Impulsiveness Scale 11 (BIS) scores. Subjects underwent 18-fluorodeoxyglucose positron emission tomography (FDG-PET) and the voxel-wise group difference of FDG-metabolism was analyzed in Statistical Parametric Mapping (SPM8). Subsequently, we performed a partial correlation analysis between the FDG-metabolism and BIS scores, controlling for covariates (i.e., age, sex, severity of disease and levodopa equivalent daily doses). Voxel-wise group comparison revealed higher FDG-metabolism in the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and right insula in patients with higher impulsivity scores. Moreover, there was a positive correlation between the FDG-metabolism and BIS scores. Our findings provide evidence that high impulsivity is associated with increased FDG-metabolis

Comparing distribution of harbour porpoise using generalized additive models and hierarchical Bayesian models with integrated nested laplace approximation

Species Distribution Models (SDMs) are used regularly to develop management strategies, but many modelling methods ignore the spatial nature of data. To address this, we compared fine-scale spatial distribution predictions of harbour porpoise (Phocoena phocoena) using empirical aerial-video-survey data collected along the east coast of Scotland in August and September 2010 and 2014. Incorporating environmental covariates that cover habitat preferences and prey proxies, we used a traditional (and commonly implemented) Generalized Additive Model (GAM), and two Hierarchical Bayesian Modelling (HBM) approaches using Integrated Nested Laplace Approxi�mation (INLA) model-fitting methodology. One HBM-INLA modelled gridded space (similar to the GAM), and the other dealt more explicitly in continuous space using a Log-Gaussian Cox Process (LGCP). Overall, predicted distributions in the three models were similar; however, HBMs had twice the level of certainty, showed much finer-scale patterns in porpoise distribution, and identified some areas of high relative density that were not apparent in the GAM. Spatial differences were due to how the two methods accounted for autocorrelation, spatial clustering of animals, and differences between modelling in discrete vs. continuous space; consequently, methods for spatial analyses likely depend on scale at which results, and certainty, are needed. For large-scale analysis (>5–10 km resolution, e.g. initial impact assessment), there was little difference be�tween results; however, insights into fine-scale (<1 km) distribution of porpoise from the HBM model using LGCP, while more computationally costly, offered potential benefits for refining conservation management or mitigation measures within offshore developments or protected areas

The COMET Handbook: version 1.0

The selection of appropriate outcomes is crucial when designing clinical trials in order to compare the effects of different interventions directly. For the findings to influence policy and practice, the outcomes need to be relevant and important to key stakeholders including patients and the public, health care professionals and others making decisions about health care. It is now widely acknowledged that insufficient attention has been paid to the choice of outcomes measured in clinical trials. Researchers are increasingly addressing this issue through the development and use of a core outcome set, an agreed standardised collection of outcomes which should be measured and reported, as a minimum, in all trials for a specific clinical area. Accumulating work in this area has identified the need for guidance on the development, implementation, evaluation and updating of core outcome sets. This Handbook, developed by the COMET Initiative, brings together current thinking and methodological research regarding those issues. We recommend a four-step process to develop a core outcome set. The aim is to update the contents of the Handbook as further research is identified

Plasma concentrations of coffee polyphenols and plasma biomarkers of diabetes risk in healthy Japanese women

Coffee consumption has been reported to reduce the risk of type 2 diabetes in experimental and epidemiological studies. This anti-diabetic effect of coffee may be attributed to its high content in polyphenols especially caffeic acid and chlorogenic acid. However, the association between plasma coffee polyphenols and diabetic risks has never been investigated in the literature. In this study, fasting plasma samples were collected from 57 generally healthy females aged 38-73 (mean 52, s.d. 8) years recruited in Himeji, Japan. The concentrations of plasma coffee polyphenols were determined by liquid chromatography coupled with mass tandem spectrometer. Diabetes biomarkers in the plasma/serum samples were analysed by a commercial diagnostic laboratory. Statistical associations were assessed using Spearman's correlation coefficients. The results showed that plasma chlorogenic acid exhibited negative associations with fasting blood glucose, glycated hemoglobin and C-reactive protein, whereas plasma total coffee polyphenol and plasma caffeic acid were weakly associated with these biomarkers. Our preliminary data support previous findings that coffee polyphenols have anti-diabetic effects but further replications with large samples of both genders are recommended

Wheldone Revisited: Structure Revision via DFT-GIAO chemical shift calculations, 1,1-HD-ADEQUATE NMR Spectroscopy, and X-ray Crystallography Studies

Wheldone was reported recently as a fungal metabolite isolated from the co-culture of Aspergillus fischeri and Xylaria flabelliformis, and it displayed cytotoxic activity against breast, melanoma, and ovarian cancer cell lines. Initially, its structure was characterized as an unusual 5-methyl-bicyclo[5.4.0]undec-3,5-diene scaffold with a 2‑hydroxy-1-propanone side chain and a 3-(2-(1-hydroxyethyl)-2-methyl-2,5-dihydrofuran-3-yl)acrylic acid moiety. Upon further examination, minor inconsistencies in the data suggested the need for structural revision. Thus, the structure of wheldone has been revisited herein using an orthogonal experimental-computational approach, which combines 1,1-HD-ADEQUATE NMR experiments, DFT-GIAO chemical shift calculations, and single crystal X-ray diffraction (SCXRD) analysis of a semi-synthetic p‑bromobenzylamide derivative, formed via a Steglich-type reaction. The summation of these data, in conjunction with previously reported Mosher’s ester analysis, now permit the unequivocal assignment of both the structure and absolute configuration of the natural product

Recommendations for exercise adherence measures in musculoskeletal settings : a systematic review and consensus meeting (protocol)

Background: Exercise programmes are frequently advocated for the management of musculoskeletal disorders; however, adherence is an important pre-requisite for their success. The assessment of exercise adherence requires the use of relevant and appropriate measures, but guidance for appropriate assessment does not exist. This research will identify and evaluate the quality and acceptability of all measures used to assess exercise adherence within a musculoskeletal setting, seeking to reach consensus for the most relevant and appropriate measures for application in research and/or clinical practice settings. Methods/design: There are two key stages to the proposed research. First, a systematic review of the quality and acceptability of measures used to assess exercise adherence in musculoskeletal disorders; second, a consensus meeting. The systematic review will be conducted in two phases and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a robust methodology. Phase one will identify all measures that have been used to assess exercise adherence in a musculoskeletal setting. Phase two will seek to identify published and unpublished evidence of the measurement and practical properties of identified measures. Study quality will be assessed against the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidelines. A shortlist of best quality measures will be produced for consideration during stage two: a meeting of relevant stakeholders in the United Kingdom during which consensus on the most relevant and appropriate measures of exercise adherence for application in research and/or clinical practice settings will be sought. Discussion: This study will benefit clinicians who seek to evaluate patients’ levels of exercise adherence and those intending to undertake research, service evaluation, or audit relating to exercise adherence in the musculoskeletal field. The findings will impact upon new research studies which aim to understand the factors that predict adherence with exercise and which test different adherence-enhancing interventions. PROSPERO reference: CRD4201300621