Patterns of alcohol consumption and related behaviour in Great Britain: a latent class analysis of the alcohol use disorder identification test (AUDIT)

Aims: Attempts have been made to develop typologies to classify different types of alcoholism. However, limited research has focused on classifications to describe general patterns of alcohol use in general population samples. Methods: Latent class analysis was used to create empirically derived behaviour clusters of alcohol consumption and related problems from the Alcohol Use Disorder Identification Test (AUDIT) based on data from a large stratified multi-stage random sample of the population of Great Britain. Multinomial logistic regression was performed to describe these resultant classes using both demographic variables and mental health outcomes. Results: Six classes best described responses in the sample data. Three were heavy consumption groups, one with multiple negative consequences, one experiencing alcohol-related injury and social pressures to cut down and an additional class with memory loss. There was one moderate class with few negative consequences, and finally two mild consumption groups, one with alcohol-related injury and social pressure to cut down and one with no associated problems. Conclusions: Alcohol use in Great Britain can be hypothesized as reflecting six distinct classes, four of which follow a continuum of increased consumption leading to increased dependence and related problems and two that do not. Differences between alcohol use classes are apparent with reduced risk of depressive episode in moderate classes and an increased risk of anxiety disorders for the highest consumers of alcohol

Agglomeration, regional grants and firm location

We examine whether discretionary government grants influence the location ofnew plants, and how effective these incentives are in the presence of agglomeration andurbanisation externalities. We find evidence that regional industrial structure affects thelocation of new entrants. Firms in more agglomerated industries locate new plants near toothers in the same industry. Firms are also attracted to industrially diversified locations.Foreign multinationals locate new plants near to other foreign-owned plants in the sameindustry. Fiscal incentives in the form of grants are found to have some effect in attractingplants to specific geographic areas eligible for such aid. We examine whether discretionary government grants influence the location ofnew plants, and how effective these incentives are in the presence of agglomeration andurbanisation externalities. We find evidence that regional industrial structure affects thelocation of new entrants. Firms in more agglomerated industries locate new plants near toothers in the same industry. Firms are also attracted to industrially diversified locations.Foreign multinationals locate new plants near to other foreign-owned plants in the sameindustry. Fiscal incentives in the form of grants are found to have some effect in attractingplants to specific geographic areas eligible for such aid

Coupling models of cattle and farms with models of badgers for predicting the dynamics of bovine tuberculosis (TB)

Bovine TB is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5%, while the accuracy of the test employed to detect infected cattle explained less than 3% of the variance in the number of infected cattle

Networks and the epidemiology of infectious disease

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues

Normal background concentrations (NBCs) of contaminants in English soils : final project report

The British Geological Survey (BGS) has been commissioned by the Department for Environment, Food and Rural Affairs (Defra) to give guidance on what are normal levels of contaminants in English soils in support of the Part 2A Contaminated Land Statutory Guidance. This has initially been done by studying the distribution of four contaminants – arsenic, lead, benzo[a]pyrene (BaP) and asbestos – in topsoils from England. This work was extended to a further four contaminants (cadmium, copper, nickel and mercury) which enabled methodologies developed to be tested on a larger range of contaminants. The first phase of the Project gathered data sets that were: nationally extensive; systematically collected so a broad range of land uses were represented; and collected and analysed to demonstrably and acceptable levels of quality. Information on the soil contaminant concentrations in urban areas was of particular importance as the normal background is considered to be a combination of both natural and diffuse anthropogenic contributions to the soil. Issues of soil quality are most important in areas where these affect most people, namely, the urban environment. The two principal data sets used in this work are the BGS Geochemical Baseline Survey of the Environment (G-BASE) rural and urban topsoils (37,269 samples) and the English NSI (National Soil Inventory) topsoils (4,864 samples) reanalysed at the BGS laboratories by X-ray fluorescence spectrometry (XRFS) so both data sets were highly compatible. These two data sets provide results for most inorganic element contaminants, though results explored for mercury and BaP are drawn from a variety of different and much less extensive data sets