Report No. 29: The Mobility and Integration of People with Disabilities into the Labour Market

Report based on a study conducted for the European Parliament, Bonn 2010 (95 pages)

Exposure to Inhalable, Respirable, and Ultrafine Particles in Welding Fume

This investigation aims to explore determinants of exposure to particle size-specific welding fume. Area sampling of ultrafine particles (UFP) was performed at 33 worksites in parallel with the collection of respirable particles. Personal sampling of respirable and inhalable particles was carried out in the breathing zone of 241 welders. Median mass concentrations were 2.48 mg m−3 for inhalable and 1.29 mg m−3 for respirable particles when excluding 26 users of powered air-purifying respirators (PAPRs). Mass concentrations were highest when flux-cored arc welding (FCAW) with gas was applied (median of inhalable particles: 11.6 mg m−3). Measurements of particles were frequently below the limit of detection (LOD), especially inside PAPRs or during tungsten inert gas welding (TIG). However, TIG generated a high number of small particles, including UFP. We imputed measurements <LOD from the regression equation with manganese to estimate determinants of the exposure to welding fume. Concentrations were mainly predicted by the welding process and were significantly higher when local exhaust ventilation (LEV) was inefficient or when welding was performed in confined spaces. Substitution of high-emission techniques like FCAW, efficient LEV, and using PAPRs where applicable can reduce exposure to welding fume. However, harmonizing the different exposure metrics for UFP (as particle counts) and for the respirable or inhalable fraction of the welding fume (expressed as their mass) remains challenging

Exposure-Response Analyses of Asbestos and Lung Cancer Subtypes in a Pooled Analysis of Case-Control Studies

International audienceBACKGROUND:Evidence is limited regarding risk and the shape of the exposure-response curve at low asbestos exposure levels. We estimated the exposure-response for occupational asbestos exposure and assessed the joint effect of asbestos exposure and smoking by sex and lung cancer subtype in general population studies.METHODS:We pooled 14 case-control studies conducted in 1985-2010 in Europe and Canada, including 17,705 lung cancer cases and 21,813 controls with detailed information on tobacco habits and lifetime occupations. We developed a quantitative job-exposure-matrix to estimate job-, time period-, and region-specific exposure levels. Fiber-years (ff/ml-years) were calculated for each subject by linking the matrix with individual occupational histories. We fit unconditional logistic regression models to estimate odds ratios (ORs), 95% confidence intervals (CIs), and trends.RESULTS:The fully adjusted OR for ever-exposure to asbestos was 1.24 (95% CI, 1.18, 1.31) in men and 1.12 (95% CI, 0.95, 1.31) in women. In men, increasing lung cancer risk was observed with increasing exposure in all smoking categories and for all three major lung cancer subtypes. In women, lung cancer risk for all subtypes was increased in current smokers (ORs ~two-fold). The joint effect of asbestos exposure and smoking did not deviate from multiplicativity among men, and was more than additive among women.CONCLUSIONS:Our results in men showed an excess risk of lung cancer and its subtypes at low cumulative exposure levels, with a steeper exposure-response slope in this exposure range than at higher, previously studied levels. (See video abstract at, http://links.lww.com/EDE/B161.)

Respirable crystalline silica and lung cancer in community-based studies: impact of job-exposure matrix specifications on exposure–response relationships

Objectives: The quantitative job-exposure matrix SYN-JEM consists of various dimensions: job-specific estimates, region-specific estimates, and prior expert ratings of jobs by the semi-quantitative DOM-JEM. We analyzed the effect of different JEM dimensions on the exposure-response relationships between occupational silica exposure and lung cancer risk to investigate how these variations influence estimates of exposure by a quantitative JEM and associated health endpoints. Methods: Using SYN-JEM, and alternative SYN-JEM specifications with varying dimensions included, cumulative silica exposure estimates were assigned to 16 901 lung cancer cases and 20 965 controls pooled from 14 international community-based case-control studies. Exposure-response relationships based on SYN-JEM and alternative SYN-JEM specifications were analyzed using regression analyses (by quartiles and log-transformed continuous silica exposure) and generalized additive models (GAM), adjusted for age, sex, study, cigarette pack-years, time since quitting smoking, and ever employment in occupations with established lung cancer risk. Results: SYN-JEM and alternative specifications generated overall elevated and similar lung cancer odds ratios ranging from 1.13 (1st quartile) to 1.50 (4th quartile). In the categorical and log-linear analyses SYN-JEM with all dimensions included yielded the best model fit, and exclusion of job-specific estimates from SYN-JEM yielded the poorest model fit. Additionally, GAM showed the poorest model fit when excluding job-specific estimates. Conclusion: The established exposure-response relationship between occupational silica exposure and lung cancer was marginally influenced by varying the dimensions of SYN-JEM. Optimized modelling of exposure-response relationships will be obtained when incorporating all relevant dimensions, namely prior rating, job, time, and region. Quantitative job-specific estimates appeared to be the most prominent dimension for this general population JEM

Occupational Benzene Exposure and Lung Cancer Risk: A Pooled Analysis of 14 Case-Control Studies.

RationaleBenzene has been classified as carcinogenic to humans, but there is limited evidence linking benzene exposure to lung cancer.ObjectivesWe aimed to examine the relationship between occupational benzene exposure and lung cancer.MethodsSubjects from 14 case-control studies across Europe and Canada were pooled. We used a quantitative job-exposure matrix to estimate benzene exposure. Logistic regression models assessed lung cancer risk across different exposure indices. We adjusted for smoking and five main occupational lung carcinogens and stratified analyses by smoking status and lung cancer subtypes.Measurements and main resultsAnalyses included 28048 subjects (12329 cases, 15719 controls). Lung cancer odds ratios ranged from 1.12 (95% CI: 1.03-1.22) to 1.32 (95% CI: 1.18-1.48) (Ptrend=0.002) for groups with the lowest and highest cumulative occupational exposure, respectively, compared to unexposed subjects. We observed an increasing trend of lung cancer with longer duration of exposure (PtrendPtrend=0.02). These effects were seen for all lung cancer subtypes, regardless of smoking status, and were not influenced by specific occupational groups, exposures, or studies.ConclusionWe found consistent and robust associations between different dimensions of occupational benzene exposure and lung cancer after adjusting for smoking and main occupational lung carcinogens. These associations were observed across different subgroups, including non-smokers. Our findings support the hypothesis that occupational benzene exposure increases the risk of developing lung cancer. Consequently, there is a need to revisit published epidemiological and molecular data on the pulmonary carcinogenicity of benzene

Occupational exposure to nickel and hexavalent chromium and the risk of lung cancer in a pooled analysis of case-control studies (SYNERGY)

There is limited evidence regarding the exposure-effect relationship between lung-cancer risk and hexavalent chromium (Cr(VI)) or nickel. We estimated lung-cancer risks in relation to quantitative indices of occupational exposure to Cr(VI) and nickel and their interaction with smoking habits. We pooled 14 case-control studies from Europe and Canada, including 16 901 lung-cancer cases and 20 965 control subjects. A measurement-based job-exposure-matrix estimated job-year-region specific exposure levels to Cr(VI) and nickel, which were linked to the subjects' occupational histories. Odds ratios (OR) and associated 95% confidence intervals (CI) were calculated by unconditional logistic regression, adjusting for study, age group, smoking habits and exposure to other occupational lung carcinogens. Due to their high correlation, we refrained from mutually adjusting for Cr(VI) and nickel independently. In men, ORs for the highest quartile of cumulative exposure to CR(VI) were 1.32 (95% CI 1.19-1.47) and 1.29 (95% CI 1.15-1.45) in relation to nickel. Analogous results among women were: 1.04 (95% CI 0.48-2.24) and 1.29 (95% CI 0.60-2.86), respectively. In men, excess lung-cancer risks due to occupational Cr(VI) and nickel exposure were also observed in each stratum of never, former and current smokers. Joint effects of Cr(VI) and nickel with smoking were in general greater than additive, but not different from multiplicative. In summary, relatively low cumulative levels of occupational exposure to Cr(VI) and nickel were associated with increased ORs for lung cancer, particularly in men. However, we cannot rule out a combined classical measurement and Berkson-type of error structure, which may cause differential bias of risk estimates

Statistical methods for the analysis of left-censored variables [Statistische Analysemethoden für linkszensierte Variablen und Beobachtungen mit Werten unterhalb einer Bestimmungs- oder Nachweisgrenze]

[english] In some applications statisticians are confronted with values which are reported to be below a limit of detection or quantitation. These left-censored variables are a challenge in the statistical analysis. In a simulation study, we compare different methods to deal with this type of data in statistical applications. These include measures of location, dispersion, association, and statistical modeling. Our simulation study showed that the multiple imputation approach and the Tobit regression lead to unbiased estimates, whereas the naïve methods including simple substitution of non-detects lead to unreliable estimates. We illustrate the application of the multiple imputation approach and the Tobit regression with an example from occupational epidemiology. <br>[german] In der statistischen Praxis treten immer wieder Variablen mit Werten unterhalb einer Bestimmungs- oder Nachweisgrenze auf. Diese sind linkszensiert und stellen daher eine Herausforderung für die statistische Analyse dar. Im Rahmen einer Simulationsstudie vergleichen wir Schätzmethoden zur Berechnung von Lage- und Streuungmaßen, Korrelationen und Regressionsparametern bei diesen Variablen. Unsere Ergebnisse zeigen, dass die multiple Imputationsmethode und die Tobit Regression zu unverzerrten Schätzungen führen. Naive Methoden, einschließlich der einfachen Substitution von zensierten Beobachtungen, ergeben hingegen unzuverlässige Schätzungen. Wir illustrieren die Anwendung der multiplen Imputationsmethode und der Tobit Regression anhand eines Beispiels aus der Epidemiologie der Arbeitswelt