984 resultados para Great Britain. Court of Chancery.


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Background: Although mortality and health inequalities at birth have increased both geographically and in socioeconomic terms, little is known about inequalities at age 85, the fastest growing sector of the population in Great Britain (GB).

Aim: To determine whether trends and drivers of inequalities in life expectancy (LE) and disability-free life expectancy (DFLE) at age 85 between 1991 and 2001 are the same as those at birth.

Methods: DFLE at birth and age 85 for 1991 and 2001 by gender were calculated for each local authority in GB using the Sullivan method. Regression modelling was used to identify area characteristics (rurality, deprivation, social class composition, ethnicity, unemployment, retirement migration) that could explain inequalities in LE and DFLE.

Results: Similar to values at birth, LE and DFLE at age 85 both increased between 1991 and 2001 (though DFLE increased less than LE) and gaps across local areas widened (and more for DFLE than LE). The significantly greater increases in LE and DFLE at birth for less-deprived compared with more-deprived areas were still partly present at age 85. Considering all factors, inequalities in DFLE at birth were largely driven by social class composition and unemployment rate, but these associations appear to be less influential at age 85.

Conclusions: Inequalities between areas in LE and DFLE at birth and age 85 have increased over time though factors explaining inequalities at birth (mainly social class and unemployment rates) appear less important for inequalities at age 85.

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1. The prediction and mapping of climate in areas between climate stations is of increasing importance in ecology.

2. Four categories of model, simple interpolation, thin plate splines, multiple linear regression and mixed spline-regression, were tested for their ability to predict the spatial distribution of temperature on the British mainland. The models were tested by external cross-verification.

3. The British distribution of mean daily temperature was predicted with the greatest accuracy by using a mixed model: a thin plate spline fitted to the surface of the country, after correction of the data by a selection from 16 independent topographical variables (such as altitude, distance from the sea, slope and topographic roughness), chosen by multiple regression from a digital terrain model (DTM) of the country.

4. The next most accurate method was a pure multiple regression model using the DTM. Both regression and thin plate spline models based on a few variables (latitude, longitude and altitude) only were comparatively unsatisfactory, but some rather simple methods of surface interpolation (such as bilinear interpolation after correction to sea level) gave moderately satisfactory results. Differences between the methods seemed to be dependent largely on their ability to model the effect of the sea on land temperatures.

5. Prediction of temperature by the best methods was greater than 95% accurate in all months of the year, as shown by the correlation between the predicted and actual values. The predicted temperatures were calculated at real altitudes, not subject to sea-level correction.

6. A minimum of just over 30 temperature recording stations would generate a satisfactory surface, provided the stations were well spaced.

7. Maps of mean daily temperature, using the best overall methods are provided; further important variables, such as continentality and length of growing season, were also mapped. Many of these are believed to be the first detailed representations at real altitude.

8. The interpolated monthly temperature surfaces are available on disk.

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The 'direct costs' attributable to 30 different endemic diseases of farm animals in Great Britain are estimated using a standardised method to construct a simple model for each disease that includes consideration of disease prevention and treatment costs. The models so far developed provide a basis for further analyses including cost-benefit analyses for the economic assessment of disease control options. The approach used reflects the inherent livestock disease information constraints, which limit the application of other economic analytical methods. It is a practical and transparent approach that is relatively easily communicated to veterinary scientists and policy makers. The next step is to develop the approach by incorporating wider economic considerations into the analyses in a way that will demonstrate to policy makers and others the importance of an economic perspective to livestock disease issues.

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Aims: To estimate the proportions of farms on which broilers, turkeys and pigs were shedding fluoroquinolone (FQ)-resistant Escherichia coli or Campylobacter spp. near to slaughter. Methods and Results: Freshly voided faeces were collected on 89 poultry and 108 pig farms and cultured with media containing 1.0 mg l(-1) ciprofloxacin. Studies demonstrated the specificity of this sensitive method, and both poultry and pig sampling yielded FQ-resistant E. coli on 60% of farms. FQ-resistant Campylobacter spp. were found on around 22% of poultry and 75% of pig farms. The majority of resistant isolates of Campylobacter (89%) and E. coli (96%) tested had minimum inhibitory concentrations for ciprofloxacin of >= 8 mg l(-1). The proportion of resistant E. coli and Campylobacter organisms within samples varied widely. Conclusions: FQ resistance is commonly present among two enteric bacterial genera prevalent on pig and poultry farms, although the low proportion of resistant organisms in many cases requires a sensitive detection technique. Significance and Impact of the Study: FQ-resistant bacteria with zoonotic potential appear to be present on a high proportion of UK pig and poultry farms. The risk this poses to consumers relative to other causes of FQ-resistant human infections remains to be clarified.

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To investigate the occurrence of antimicrobial resistance genes of human clinical relevance in Salmonella isolated from livestock in Great Britain. Two hundred and twenty-five Salmonella enterica isolates were characterized using an antimicrobial resistance gene chip and disc diffusion assays. Plasmid profiling, conjugation experiments and identification of Salmonella genomic island 1 (SGI1) were performed for selected isolates. Approximately 43% of Salmonella harboured single or multiple antimicrobial resistance genes with pig isolates showing the highest numbers where 96% of Salmonella Typhimurium harboured one or more resistance genes. Isolates harbouring multiple resistances divided into three groups. Group 1 isolates harboured ampicillin/streptomycin/sulphonamide/tetracycline resistance and similar phenotypes. This group contained isolates from pigs, cattle and poultry that were from several serovars including Typhimurium, 4,[5],12:i:-, Derby, Ohio and Indiana. All Group 2 isolates were from pigs and were Salmonella Typhimurium. They contained a non-sul-type class 1 integron and up to 13 transferrable resistances. All Group 3 isolates harboured a class 1 integron and were isolated from all animal species included in the study. Most isolates were Salmonella Typhimurium and harboured SGI1. Salmonella isolated from livestock was shown to harbour antimicrobial resistance genes although no or little resistance to third-generation cephalosporins or ciprofloxacin, respectively, was detected. The preponderance in pigs of multidrug-resistant Salmonella Typhimurium makes it important to introduce control measures such as improved biosecurity to ensure that they do not pass through the food chain and limit human therapeutic options.

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Total phosphorus (TP) and soluble reactive phosphorus (SRP) loads to watercourses of the River Basin Districts (RBDs) of Great Britain (GB) were estimated using inventories of industrial P loads and estimates of P loads from sewage treatment works and diffuse P loads calculated using region-specific export coefficients for particular land cover classes combined with census data for agricultural stocking densities and human populations. The TP load to GB waters was estimated to be 60 kt yr(-1), of which households contributed 73, agriculture contributed 20, industry contributed 3, and 4 came from background sources. The SRP load to GB waters was estimated to be 47 kt yr(-1), of which households contributed 78, agriculture contributed 13, industry contributed 4, and 6 came from background Sources. The 'average' area-normalized TP and SRP loads to GB waters approximated 2.4 kg ha(-1) yr(-1) and 1.8 kg ha(-1) yr(-1), respectively. A consideration of uncertainties in the data contributing to these estimates suggested that the TP load to GB waters might lie between 33 and 68 kt yr(-1), with agriculture contributing between 10 and 28 of the TP load. These estimates are consistent with recent appraisals of annual TP and SRP loads to GB coastal waters and area-normalized TP loads from their catchments. Estimates of the contributions of RBDs to these P loads were consistent with the geographical distribution of P concentrations in GB rivers and recent assessments of surface waters at risk from P Pollution.

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The MATLAB model is contained within the compressed folders (versions are available as .zip and .tgz). This model uses MERRA reanalysis data (>34 years available) to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms. A ready made example is included for the wind farm distribution of Great Britain, April 2014 ("CF.dat"). This consists of an hourly time series of GB-total capacity factor spanning the period 1980-2013 inclusive. Given the global nature of reanalysis data, the model can be applied to any specified distribution of wind farms in any region of the world. Users are, however, strongly advised to bear in mind the limitations of reanalysis data when using this model/data. This is discussed in our paper: Cannon, Brayshaw, Methven, Coker, Lenaghan. "Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain". Submitted to Renewable Energy in March, 2014. Additional information about the model is contained in the model code itself, in the accompanying ReadMe file, and on our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/

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Description based on: 1907.

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Printed in Great Britain.

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Mode of access: Internet.