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Exposure to air pollution has been associated with the loss of lung function in epidemiologic studies. In this study, exposures of individual children were assessed through the measurement of carbon in macrophages and were shown to be related to exposure to local pollution and to lung function. Exposures of individual children were assessed through the measurement of carbon in macrophages and were shown to be related to exposure to local pollution and to lung function. Black carbon is a major component of inhalable particulate matter (particulate matter <10 μm in aerodynamic diameter [PM 10 ]) directly emitted from the combustion of fossil fuels. 1 Black carbon consists of a carbon core enriched with trace metals and organic compounds, and it is thought to mediate many of the adverse health effects reported in epidemiologic studies to be associated with PM 10 . 2 Children are especially vulnerable to the adverse effects of PM 10 , 2 with the cumulative effects on the growth of lung function of particular concern. For example, Gauderman et al. 3 studied air pollution data from . . .

The UK has had universal free health care and public health programmes for more than six decades. Several policy initiatives and structural reforms of the health system have been undertaken. Health expenditure has increased substantially since 1990, albeit from relatively low levels compared with other countries. We used data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010) to examine the patterns of health loss in the UK, the leading preventable risks that explain some of these patterns, and how UK outcomes compare with a set of comparable countries in the European Union and elsewhere in 1990 and 2010. We used results of GBD 2010 for 1990 and 2010 for the UK and 18 other comparator nations (the original 15 members of the European Union, Australia, Canada, Norway, and the USA; henceforth EU15+). We present analyses of trends and relative performance for mortality, causes of death, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE). We present results for 259 diseases and injuries and for 67 risk factors or clusters of risk factors relevant to the UK. We assessed the UK's rank for age-standardised YLLs and DALYs for their leading causes compared with EU15+ in 1990 and 2010. We estimated 95% uncertainty intervals (UIs) for all measures. For both mortality and disability, overall health has improved substantially in absolute terms in the UK from 1990 to 2010. Life expectancy in the UK increased by 4·2 years (95% UI 4·2–4·3) from 1990 to 2010. However, the UK performed significantly worse than the EU15+ for age-standardised death rates, age-standardised YLL rates, and life expectancy in 1990, and its relative position had worsened by 2010. Although in most age groups, there have been reductions in age-specific mortality, for men aged 30–34 years, mortality rates have hardly changed (reduction of 3·7%, 95% UI 2·7–4·9). In terms of premature mortality, worsening ranks are most notable for men and women aged 20–54 years. For all age groups, the contributions of Alzheimer's disease (increase of 137%, 16–277), cirrhosis (65%, −15 to 107), and drug use disorders (577%, 71–942) to premature mortality rose from 1990 to 2010. In 2010, compared with EU15+, the UK had significantly lower rates of age-standardised YLLs for road injury, diabetes, liver cancer, and chronic kidney disease, but significantly greater rates for ischaemic heart disease, chronic obstructive pulmonary disease, lower respiratory infections, breast cancer, other cardiovascular and circulatory disorders, oesophageal cancer, preterm birth complications, congenital anomalies, and aortic aneurysm. Because YLDs per person by age and sex have not changed substantially from 1990 to 2010 but age-specific mortality has been falling, the importance of chronic disability is rising. The major causes of YLDs in 2010 were mental and behavioural disorders (including substance abuse; 21·5% [95 UI 17·2–26·3] of YLDs), and musculoskeletal disorders (30·5% [25·5–35·7]). The leading risk factor in the UK was tobacco (11·8% [10·5–13·3] of DALYs), followed by increased blood pressure (9·0 % [7·5–10·5]), and high body-mass index (8·6% [7·4–9·8]). Diet and physical inactivity accounted for 14·3% (95% UI 12·8–15·9) of UK DALYs in 2010. The performance of the UK in terms of premature mortality is persistently and significantly below the mean of EU15+ and requires additional concerted action. Further progress in premature mortality from several major causes, such as cardiovascular diseases and cancers, will probably require improved public health, prevention, early intervention, and treatment activities. The growing burden of disability, particularly from mental disorders, substance use, musculoskeletal disorders, and falls deserves an integrated and strategic response. Bill & Melinda Gates Foundation.

Purpose of Review Burden of occupational disease estimation contributes to understanding of both magnitude and relative importance of different occupational hazards and provides essential information for targeting risk reduction. This review summarises recent key findings and discusses their impact on occupational regulation and practice. Recent Findings New methods have been developed to estimate burden of occupational disease that take account of the latency of many chronic diseases and allow for exposure trends and workforce turnover. Results from these studies have shown in several countries and globally that, in spite of improvements in workplace technology, practices and exposures over the last decades, occupational hazards remain an important cause of ill health and mortality worldwide. Summary Major data gaps have been identified particularly regarding exposure information. Reliable data on employment and disease are also lacking especially in developing countries. Burden of occupational disease estimates form an important part of decision-making processes.

Objectives The standard approach to the assessment of occupational exposures is through the manual collection and coding of job histories. This method is time-consuming and costly and makes it potentially unfeasible to perform high quality analyses on occupational exposures in large population-based studies. Our aim was to develop a novel, efficient web-based tool to collect and code lifetime job histories in the UK Biobank, a population-based cohort of over 500 000 participants. Methods We developed OSCAR (occupations self-coding automatic recording) based on the hierarchical structure of the UK Standard Occupational Classification (SOC) 2000, which allows individuals to collect and automatically code their lifetime job histories via a simple decision-tree model. Participants were asked to find each of their jobs by selecting appropriate job categories until they identified their job title, which was linked to a hidden 4-digit SOC code. For each occupation a job title in free text was also collected to estimate Cohen's kappa (κ) inter-rater agreement between SOC codes assigned by OSCAR and an expert manual coder. Results OSCAR was administered to 324 653 UK Biobank participants with an existing email address between June and September 2015. Complete 4-digit SOC-coded lifetime job histories were collected for 108 784 participants (response rate: 34%). Agreement between the 4-digit SOC codes assigned by OSCAR and the manual coder for a random sample of 400 job titles was moderately good [κ=0.45, 95% confidence interval (95% CI) 0.42–0.49], and improved when broader job categories were considered (κ=0.64, 95% CI 0.61–0.69 at a 1-digit SOC-code level). Conclusions OSCAR is a novel, efficient, and reasonably reliable web-based tool for collecting and automatically coding lifetime job histories in large population-based studies. Further application in other research projects for external validation purposes is warranted.

IntroductionEvery year, large numbers of workers have an injury at work or develop a work-related disease. A range of worker compensation systems exist across countries for which epidemiological studies potentially provide critical evidence. This paper discusses the adequacy of current epidemiological research for this purpose.MethodsCompensation schemes need to identify the occupational circumstances, or dose, that increase (e.g. double) the risks of the disease, and to define these so they can be administered effectively by decision-makers who lack epidemiological experience. Large studies or meta-analyses with effective control for confounders, adequate exposure assessment, and clear case definition are (ideally) required.ResultsAlthough epidemiological studies may show consistent evidence of an increased health risk from an occupational exposure, definitions of exposure are often unsuitable for converting into an appropriate exposure schedule for a compensation scheme. Direct measurements of occupational exposure are usually scarce and not available for individual workers; both claimants, perhaps, assessors would find these measurements difficult to access and use. Exposures defined by industry or job title, particularly with a qualifying time-period, although perceived by epidemiologists as rather simplistic, are more straightforward to use, e.g. ‘osteoarthritis in miners who have worked 10 or more years underground’. If quantitative exposure measurements are unavailable, epidemiologists often define qualitative metrics such as ‘high’, ‘medium’, or ‘low’ or develop more complex semi-qualitative exposure metrics such as ‘exposed or not’ at a given level of certainty or probability; intensity of exposure based on expert judgement of proximity to the substance and effectiveness of control procedures. Application of these in a compensation scheme may be problematical.DiscussionEpidemiology plays a vital role in ensuring workers are compensated for work-related ill-health. Epidemiological study design, exposure metrics and primary and subsidiary analyses should be tailored to directly support compensation schemes.

IntroductionSeveral recent occupational burden estimation studies have identifyed major risk factors contributing to important morbidity burdens. This paper discusses their impact.MethodsEuropean studies include (1) the British occupational cancer burden study and (2) an EU socio-economic health impact assessment of introducing binding occupational exposure limits (OEL) for 25 workplace carcinogens. The global burden of occupational disease project (3) includes estimation for carcinogens, asthmagens, particulate matter, noise, and risk factors for low back pain and injury.ResultsThe British study (1) has informed the Health and Safety Executive’s long latency programme and their guidance and practical interventions for risk reduction. The results have facilitated estimation of the financial impact of these cancers; the majority of the cost is borne by workers. It has also contributed to the successful Institution for Occupational Safety and Health ‘No time to lose’ campaigns to help industry to deliver effective workplace cancer prevention programmes.The EU study (2) illustrates the use of cost/benefit analyses in OEL decision making processes. ‘Efficient’ cost/benefit ratios and ‘disproportionate’ compliance costs to small/medium sized enterprises are weighed against health-based predictions.The global burden study (3) highlights inequalities in work-related disease burden between countries.DiscussionOccupational burden studies increase awareness of occupational disease generally and for particular diseases and galvanise different stakeholders to work together on prevention. They highlight potential inequalities to different sectors of society. However, they can be ‘burdensome’ regarding cost and effort and debate is needed on timing of and appropriate methods for future updates.

Background and objectivesWe aimed to estimate credibility intervals for the British occupational cancer burden to account for bias uncertainty, using a method adapted from Greenland’s Monte Carlo sensitivity analysis.MethodsThe attributable fraction (AF) methodology used for our cancer burden estimates requires risk estimates and population proportions exposed for each agent/cancer pair. Sources of bias operating on AF estimator components include non-portability of risk estimates, inadequate models, inaccurate data including unknown cancer latency and employment turnover and compromises in using the available estimators. Each source of bias operates on a component of the AF estimator. Independent prior distributions were estimated for each bias, or graphical sensitivity analysis was used to identify plausible distribution ranges for the component variables, with AF recalculated following Monte Carlo repeated sampling from these distributions. The methods are illustrated using the example of lung cancer due to occupational exposure to respirable crystalline silica in men.ResultsResults are presented graphically for a hierarchy of biases contributing to an overall credibility interval for lung cancer and respirable crystalline silica exposure. An overall credibility interval of 2.0% to 16.2% was estimated for an AF of 3.9% in men. Choice of relative risk and employment turnover were shown to contribute most to overall estimate uncertainty. Bias from using an incorrect estimator makes a much lower contribution.ConclusionsThe method illustrates the use of credibility intervals to indicate relative contributions of important sources of uncertainty and identifies important data gaps; results depend greatly on the priors chosen.

Background In Great Britain (GB), data collected on pesticide associated illness focuses on acute episodes such as poisonings caused by misuse or abuse. This study aimed to investigate the extent and nature of pesticide-related illness presented and diagnosed in Primary Care and the feasibility of establishing a routine monitoring system. Methods A checklist, completed by General Practitioners (GP) for all patients aged 18+ who attended surgery sessions, identified patients to be interviewed in detail on exposures and events that occurred in the week before their symptoms appeared. Results The study covered 59320 patients in 43 practices across GB and 1335 detailed interviews. The annual incidence of illness reported to GPs because of concern about pesticide exposure was estimated to be 0.04%, potentially 88400 consultations annually, approximately 1700 per week. The annual incidence of consultations where symptoms were diagnosed by GPs as likely to be related to pesticide exposure was 0.003%, an annual estimate of 6630 consultations i.e. about 128 per week. 41% of interviewees reported using at least one pesticide at home in the week before symptoms occurred. The risk of having symptoms possibly related to pesticide exposure compared to unlikely was associated with home use of pesticides after adjusting for age, gender and occupational pesticide exposure (OR = 1.88, 95% CI 1.51 – 2.35). Conclusion GP practices were diverse and well distributed throughout GB with similar symptom consulting patterns as in the Primary Care within the UK. Methods used in this study would not be feasible for a routine surveillance system for pesticide related illness. Incorporation of environmental health into Primary Care education and practice is needed.

To present the contour-enhanced funnel plot as an aid to differentiating asymmetry due to publication bias from that due to other factors. An enhancement to the usual funnel plot is proposed that allows the statistical significance of study estimates to be considered. Contour lines indicating conventional milestones in levels of statistical significance (e.g., <0.01, <0.05, <0.1) are added to funnel plots. This contour overlay aids the interpretation of the funnel plot. For example, if studies appear to be missing in areas of statistical nonsignificance, then this adds credence to the possibility that the asymmetry is due to publication bias. Conversely, if the supposed missing studies are in areas of higher statistical significance, this would suggest the cause of the asymmetry may be more likely to be due to factors other than publication bias, such as variable study quality. We believe this enhancement to funnel plots (i) is simple to implement, (ii) is widely applicable, (iii) greatly improves interpretability, and (iv) should be used routinely.