Background:Mesothelioma mortality has increased more than ten-fold over the past 40 years in Great Britain, with >1700 male deaths recorded in the British mesothelioma register in 2006. Annual mesothelioma deaths now account for >1% of all cancer deaths. A Poisson regression model based on a previous work by Hodgson et al has been fitted, which has allowed informed statistical inferences about model parameters and predictions of future mesothelioma mortality to be made.Methods:In the Poisson regression model, the mesothelioma risk of an individual depends on the average collective asbestos dose for the individual in a given year and an age-specific exposure potential. The model has been fitted to the data within a Bayesian framework using the Metropolis–Hastings algorithm, a Markov Chain Monte Carlo technique, providing credible intervals for model parameters as well as prediction intervals for the number of future cases of mortality.Results:Males were most likely to have been exposed to asbestos between the ages of 30 and 49 years, with the peak year of asbestos exposure estimated to be 1963. The estimated number of background cases was 1.08 cases per million population.Conclusion:Mortality among males is predicted to peak at approximately 2040 deaths in the year 2016, with a rapid decline thereafter. Approximately 91 000 deaths are predicted to occur from 1968 to 2050 with around 61 000 of these occurring from 2007 onwards.

levels for 61 elements were established in urine samples collected from 132 occupationally unexposed UK adults. In this study all elements were determined by inductively coupled plasma-mass spectrometry, but methods were 'tailored' to the elements; in total six analytical methods were undertaken. For the first time in a UK population 95th percentile values are reported for 19 elements for which there is no available comparison. Repeat urine samples were collected from some individuals and mixed effects modelling was carried out on the data to give an estimation of variation both between individuals and within the same individual. The mixed effects modelling was undertaken on 31 of the 61 elements for which there were more than two thirds of data above the LOQ and variations of between and within individuals are reported. The analysis found that creatinine adjustment of analyte concentrations was found to be beneficial for 22 of the 31 elements and that smokers were found to exhibit significantly higher cadmium but lower boron than non-smokers. For most elements, the data compare well with other published data but higher concentrations were observed in this study for urinary lead, chromium, vanadium and tungsten.

Hypertension (HT) is associated with environmental noise exposure and is a risk factor for a range of health outcomes. The study aims were to identify key HT related health outcomes and to quantify and monetize the impact on health outcomes attributable to environmental noise-related HT. A reiterative literature review identified key HT related health outcomes and their quantitative links with HT. The health impact of increases in environmental noise above recommended daytime noise levels (55 dB[A]) were quantified in terms of quality adjusted life years and then monetized. A case study evaluated the cost of environmental noise, using published data on health risks and the number of people exposed to various bands of environmental noise levels in the United Kingdom (UK). Three health outcomes were selected based on the strength of evidence linking them with HT and their current impact on society: Acute myocardial infarction (AMI), stroke and dementia. In the UK population, an additional 542 cases of HT-related AMI, 788 cases of stroke and 1169 cases of dementia were expected per year due to daytime noise levels ≥55 dB(A). The cost of these additional cases was valued at around £1.09 billion, with dementia accounting for 44%. The methodology is dependent on the availability and quality of published data and the resulting valuations reflect these limitations. The estimated intangible cost provides an insight into the scale of the health impacts and conversely the benefits that the implementation of policies to manage environmental noise may confer.

The aim of this study was to establish concentrations of a wide range of elements in human lung samples to allow better identification of potential exposures in subsequent cases. This study reports concentrations of 48 elements (Al, As, Au, B, Ba, Be, Bi, Br, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Gd, Ge, Hf, Hg, In, Li, Mn, Mo, Nb, Ni, Os, Pb, Pd, Pt, Rb, Re, Ru, Sb, Se, Sm, Sn, Sr, Ta, Te, Ti, Tl, Tm, V, W, Y, Zn and Zr) in fresh lung tissue samples from 54 hospital patients, of which 93% exhibited various forms of neoplasia. The lung samples were taken from unaffected, background tissue. The samples were stored as fresh tissue in alcohol, dried and microwave digested before analysis by inductively coupled mass spectrometry (ICP-MS). It was possible to establish 95th percentiles for all elements except for rhenium and for 40 elements mixed effects modelling was undertaken. Overall, the levels reported are commensurate with ranges for those elements that had been reported previously. The data were examined for gender, smoking and occupational exposures to metals. The results show that males have higher lung concentrations of Ni, Cr, Gd, Au and Be than females, but significantly lower lung concentrations of Co, Sn, W and In. Cadmium lung concentrations were significantly higher in smokers. Platinum lung concentrations were higher in those who had undergone chemotherapy and gadolinium concentrations were predictably high in those who had undergone imaging scans. More essential elements such as Cu, Br, Fe and also Ge varied the least within lung samples from individuals whilst Be, Hf and Pt had the greatest variances. Between individuals V and Li lung concentrations varied the most, whilst Cu varied least. Analysis of the data for those who reported as having previously worked with metals showed 24 of the 48 elements determined were higher than those from those who had not reported working with metals.

This study provides background levels for five arsenic species in urine, based on urinary data obtained from 95 nonoccupationally exposed volunteers based in the UK. Using a novel, sensitive, robust and reliable speciation methodology, five species of arsenic (arsenobetaine [AB], arsenite [As(3+)], arsenate [As(5+)], monomethylarsonic acid [MMA(5+)] and dimethylarsinic acid [DMA(5+)]) were determined in urine samples collected from 95 adults. The analytical instrumentation used to analyze the urine samples was a hyphenated micro liquid chromatography (μLC) system coupled to an inductively coupled plasma mass spectrometry (ICP-MS). Separation was achieved using an anion exchange micro-sized column. The results presented give the 95th percentile of concentrations, both uncorrected for creatinine (µg/L) and creatinine corrected (µmol/mol) in urine for the 95 volunteers. Statistical analysis was performed on the dataset using a Bayesian model to determine and quantify effects of gender, smoking and diet. The statistical results show that the consumption of fish, shellfish and red wine has a significant elevating effect on AB, DMA and MMA urinary concentrations; however, no significant effect was observed for smoking. The regression model results indicate that creatinine correction was effective for arsenic species As(3+), MMA, DMA and AB. The background levels established here can be used as reference values to help aid interpretation of arsenic speciation results and better assess exposure.