Alcohol and the Risk of Barrett's Esophagus: A Pooled Analysis from the International BEACON Consortium

OBJECTIVES: Results from studies examining the association between alcohol consumption and the risk of Barrett's esophagus have been inconsistent. We assessed the risk of Barrett's esophagus associated with total and beverage-specific alcohol consumption by pooling individual participant data from five case–control studies participating in the international Barrett's and Esophageal Adenocarcinoma Consortium.
METHODS: For analysis, there were 1,282 population-based controls, 1,418 controls with gastroesophageal reflux disease (GERD), and 1,169 patients with Barrett's esophagus (cases). We estimated study-specific odds ratios (ORs) and 95% confidence intervals (95% CI) using multivariable logistic regression models adjusted for age, sex, body mass index (BMI), education, smoking status, and GERD symptoms. Summary risk estimates were obtained by random-effects models. We also examined potential effect modification by sex, BMI, GERD symptoms, and cigarette smoking.
RESULTS: For comparisons with population-based controls, although there was a borderline statistically significant inverse association between any alcohol consumption and the risk of Barrett's esophagus (any vs. none, summary OR=0.77, 95% CI=0.60–1.00), risk did not decrease in a dose-response manner (Ptrend=0.72). Among alcohol types, wine was associated with a moderately reduced risk of Barrett's esophagus (any vs. none, OR=0.71, 95% CI=0.52–0.98); however, there was no consistent dose–response relationship (Ptrend=0.21). We found no association with alcohol consumption when cases were compared with GERD controls. Similar associations were observed across all strata of BMI, GERD symptoms, and cigarette smoking.
CONCLUSIONS: Consistent with findings for esophageal adenocarcinoma, we found no evidence that alcohol consumption increases the risk of Barrett's esophagus.

Estimating the incidence, prevalence and true cost of asthma in the UK: Secondary analysis of national stand-alone and linked databases in England, Northern Ireland, Scotland and Wales-a study protocol

Introduction Asthma is now one of the most common long-term conditions in the UK. It is therefore important to develop a comprehensive appreciation of the healthcare and societal costs in order to inform decisions on care provision and planning. We plan to build on our earlier estimates of national prevalence and costs from asthma by filling the data gaps previously identified in relation to healthcare and broadening the field of enquiry to include societal costs. This work will provide the first UK-wide estimates of the costs of asthma. In the context of asthma for the UK and its member countries (ie, England, Northern Ireland, Scotland and Wales), we seek to: (1) produce a detailed overview of estimates of incidence, prevalence and healthcare utilisation; (2) estimate health and societal costs; (3) identify any remaining information gaps and explore the feasibility of filling these and (4) provide insights into future research that has the potential to inform changes in policy leading to the provision of more cost-effective care.

Methods and analysis Secondary analyses of data from national health surveys, primary care, prescribing, emergency care, hospital, mortality and administrative data sources will be undertaken to estimate prevalence, healthcare utilisation and outcomes from asthma. Data linkages and economic modelling will be undertaken in an attempt to populate data gaps and estimate costs. Separate prevalence and cost estimates will be calculated for each of the UK-member countries and these will then be aggregated to generate UK-wide estimates.

Ethics and dissemination Approvals have been obtained from the NHS Scotland Information Services Division's Privacy Advisory Committee, the Secure Anonymised Information Linkage Collaboration Review System, the NHS South-East Scotland Research Ethics Service and The University of Edinburgh's Centre for Population Health Sciences Research Ethics Committee. We will produce a report for Asthma-UK, submit papers to peer-reviewed journals and construct an interactive map.

Effective grain size distribution analysis for interpretation of tidal-deltaic facies: West Bengal Sundarbans

Research over the past two decades on the Holocene sediments from the tide dominated west side of the lower Ganges delta has focussed on constraining the sedimentary environment through grain size distributions (GSD). GSD has traditionally been assessed through the use of probability density function (PDF) methods (e.g. log-normal, log skew-Laplace functions), but these approaches do not acknowledge the compositional nature of the data, which may compromise outcomes in lithofacies interpretations. The use of PDF approaches in GSD analysis poses a series of challenges for the development of lithofacies models, such as equifinal distribution coefficients and obscuring the empirical data variability. In this study a methodological framework for characterising GSD is presented through compositional data analysis (CODA) plus a multivariate statistical framework. This provides a statistically robust analysis of the fine tidal estuary sediments from the West Bengal Sundarbans, relative to alternative PDF approaches.

Reflection of Qualitative Methods: “memoring” fieldwork experience in organisational research with photographs

The paper is a reflection on the use of photographs in multiple case study research. It explores the crossovers between interpreting visual artefacts, the qualitative approach to case study research in organisations, and the move from cases to theory guided by the grounded theory tenets. The paper proposes an additional use of photographs as a visual method to those in the literature, as a device for data analysis. Photograph-based analysis techniques are explored, using e sequence of individual images and photo collages on case data, moving from interpretation of single to multiple case themes. This makes the case of using photograph analysis as an interpretation device for case research to illuminate theory development.

Low alanine aminotransferase levels and higher number of cardiovascular events in people with Type 2 diabetes: analysis of the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study.

AIMS: To determine whether alanine aminotransferase or gamma-glutamyltransferase levels, as markers of liver health and non-alcoholic fatty liver disease, might predict cardiovascular events in people with Type 2 diabetes.

METHODS: Data from the Fenofibrate Intervention and Event Lowering in Diabetes study were analysed to examine the relationship between liver enzymes and incident cardiovascular events (non-fatal myocardial infarction, stroke, coronary and other cardiovascular death, coronary or carotid revascularization) over 5 years.

RESULTS: Alanine aminotransferase level had a linear inverse relationship with the first cardiovascular event occurring in participants during the study period. After adjustment, for every 1 sd higher baseline alanine aminotransferase value (13.2 U/l), the risk of a cardiovascular event was 7% lower (95% CI 4-13; P=0.02). Participants with alanine aminotransferase levels below and above the reference range 8-41 U/l for women and 9-59 U/l for men, had hazard ratios for a cardiovascular event of 1.86 (95% CI 1.12-3.09) and 0.65 (95% CI 0.49-0.87), respectively (P=0.001). No relationship was found for gamma-glutamyltransferase.

CONCLUSIONS: The data may indicate that in people with Type 2 diabetes, which is associated with higher alanine aminotransferase levels because of prevalent non-alcoholic fatty liver disease, a low alanine aminotransferase level is a marker of hepatic or systemic frailty rather than health. This article is protected by copyright. All rights reserved.

Abdominal Aortic Aneurysm Genetic Associations: Mostly False? A Systematic Review and Meta-analysis

OBJECTIVE/BACKGROUND: Many associations between abdominal aortic aneurysm (AAA) and genetic polymorphisms have been reported. It is unclear which are genuine and which may be caused by type 1 errors, biases, and flexible study design. The objectives of the study were to identify associations supported by current evidence and to investigate the effect of study design on reporting associations.

METHODS: Data sources were MEDLINE, Embase, and Web of Science. Reports were dual-reviewed for relevance and inclusion against predefined criteria (studies of genetic polymorphisms and AAA risk). Study characteristics and data were extracted using an agreed tool and reports assessed for quality. Heterogeneity was assessed using I(2) and fixed- and random-effects meta-analyses were conducted for variants that were reported at least twice, if any had reported an association. Strength of evidence was assessed using a standard guideline.

RESULTS: Searches identified 467 unique articles, of which 97 were included. Of 97 studies, 63 reported at least one association. Of 92 studies that conducted multiple tests, only 27% corrected their analyses. In total, 263 genes were investigated, and associations were reported in polymorphisms in 87 genes. Associations in CDKN2BAS, SORT1, LRP1, IL6R, MMP3, AGTR1, ACE, and APOA1 were supported by meta-analyses.

CONCLUSION: Uncorrected multiple testing and flexible study design (particularly testing many inheritance models and subgroups, and failure to check for Hardy-Weinberg equilibrium) contributed to apparently false associations being reported. Heterogeneity, possibly due to the case mix, geographical, temporal, and environmental variation between different studies, was evident. Polymorphisms in nine genes had strong or moderate support on the basis of the literature at this time. Suggestions are made for improving AAA genetics study design and conduct.

Generalized collisional radiative model for light elements: C: Data for the B isonuclear sequence

A first stage collision database is assembled which contains electron-impact excitation, ionization,\r and recombination rate coefficients for B, B + , B 2+ , B 3+ , and B 4+ . The first stage database\r is constructed using the R-matrix with pseudostates, time-dependent close-coupling, and perturbative\r distorted-wave methods. A second stage collision database is then assembled which contains\r generalized collisional-radiative ionization, recombination, and power loss rate coefficients as a\r function of both temperature and density. The second stage database is constructed by solution of\r the collisional-radiative equations in the quasi-static equilibrium approximation using the first\r stage database. Both collision database stages reside in electronic form at the IAEA Labeled Atomic\r Data Interface (ALADDIN) database and the Atomic Data Analysis Structure (ADAS) open database.

Multivariate analysis of regional-scale geochemical data for environmental monitoring

A compositional multivariate approach is used to analyse regional scale soil geochemical data obtained as part of the Tellus Project generated by the Geological Survey Northern Ireland (GSNI). The multi-element total concentration data presented comprise XRF analyses of 6862 rural soil samples collected at 20cm depths on a non-aligned grid at one site per 2 km2. Censored data were imputed using published detection limits. Using these imputed values for 46 elements (including LOI), each soil sample site was assigned to the regional geology map provided by GSNI initially using the dominant lithology for the map polygon. Northern Ireland includes a diversity of geology representing a stratigraphic record from the Mesoproterozoic, up to and including the Palaeogene. However, the advance of ice sheets and their meltwaters over the last 100,000 years has left at least 80% of the bedrock covered by superficial deposits, including glacial till and post-glacial alluvium and peat. The question is to what extent the soil geochemistry reflects the underlying geology or superficial deposits. To address this, the geochemical data were transformed using centered log ratios (clr) to observe the requirements of compositional data analysis and avoid closure issues. Following this, compositional multivariate techniques including compositional Principal Component Analysis (PCA) and minimum/maximum autocorrelation factor (MAF) analysis method were used to determine the influence of underlying geology on the soil geochemistry signature. PCA showed that 72% of the variation was determined by the first four principal components (PC’s) implying “significant” structure in the data. Analysis of variance showed that only 10 PC’s were necessary to classify the soil geochemical data. To consider an improvement over PCA that uses the spatial relationships of the data, a classification based on MAF analysis was undertaken using the first 6 dominant factors. Understanding the relationship between soil geochemistry and superficial deposits is important for environmental monitoring of fragile ecosystems such as peat. To explore whether peat cover could be predicted from the classification, the lithology designation was adapted to include the presence of peat, based on GSNI superficial deposit polygons and linear discriminant analysis (LDA) undertaken. Prediction accuracy for LDA classification improved from 60.98% based on PCA using 10 principal components to 64.73% using MAF based on the 6 most dominant factors. The misclassification of peat may reflect degradation of peat covered areas since the creation of superficial deposit classification. Further work will examine the influence of underlying lithologies on elemental concentrations in peat composition and the effect of this in classification analysis.