Gefährdungsbeurteilung bei Exposition gegenüber mehreren krebserzeugenden Arbeitsstoffen

We analyzed the validity of simple summation formulae with which a common effect of different occupational exposures could be mapped to the same cancer endpoint. Our paper published in this journal relied on the assumptions of a deterministic response and binary exposures. The German Federal Environmental Agency organized an external review of our paper. Although the reviewers agreed with our main conclusion that simple summation formulae cannot have a general validity, they raised questions that we would like to inform the scientific public about and are addressed in this addendum. We demonstrate that the main statements in Morfeld and Spallek 2015 remain valid even if the exposures are defined on an interval scale, the binary response is probabilistic or if the analyzed response types are restricted to adverse types. We clarify that excess risks and differences in excess risks are not probabilities according to the Kolmogorov axioms. Simple summation formulae are therefore not justified, not even to estimate the lower bounds for excess risks

Response to the Reply on behalf of the ‘Permanent Senate Commission for the Investigation of Health Hazards of Chemical Compounds in the Work Area’ (MAK Commission) by Andrea Hartwig Karlsruhe Institute of Technology (KIT)

Prof. Hartwig commented [1] as chair of the MAK Commission on Morfeld et al. 2015 [2]. We would like to thank the Commission for commenting on our review. However, the MAK Commission did not address a number of important issues raised in our paper

Translational toxicology in setting occupational exposure limits for dusts and hazard classification – a critical evaluation of a recent approach to translate dust overload findings from rats to humans

Background We analyze the scientific basis and methodology used by the German MAK Commission in their recommendations for exposure limits and carcinogen classification of “granular biopersistent particles without known specific toxicity” (GBS). These recommendations are under review at the European Union level. We examine the scientific assumptions in an attempt to reproduce the results. MAK’s human equivalent concentrations (HECs) are based on a particle mass and on a volumetric model in which results from rat inhalation studies are translated to derive occupational exposure limits (OELs) and a carcinogen classification. Methods We followed the methods as proposed by the MAK Commission and Pauluhn 2011. We also examined key assumptions in the metrics, such as surface area of the human lung, deposition fractions of inhaled dusts, human clearance rates; and risk of lung cancer among workers, presumed to have some potential for lung overload, the physiological condition in rats associated with an increase in lung cancer risk. Results The MAK recommendations on exposure limits for GBS have numerous incorrect assumptions that adversely affect the final results. The procedures to derive the respirable occupational exposure limit (OEL) could not be reproduced, a finding raising considerable scientific uncertainty about the reliability of the recommendations. Moreover, the scientific basis of using the rat model is confounded by the fact that rats and humans show different cellular responses to inhaled particles as demonstrated by bronchoalveolar lavage (BAL) studies in both species. Conclusion Classifying all GBS as carcinogenic to humans based on rat inhalation studies in which lung overload leads to chronic inflammation and cancer is inappropriate. Studies of workers, who have been exposed to relevant levels of dust, have not indicated an increase in lung cancer risk. Using the methods proposed by the MAK, we were unable to reproduce the OEL for GBS recommended by the Commission, but identified substantial errors in the models. Considerable shortcomings in the use of lung surface area, clearance rates, deposition fractions; as well as using the mass and volumetric metrics as opposed to the particle surface area metric limit the scientific reliability of the proposed GBS OEL and carcinogen classification.International Carbon Black Associatio

Years of Life Lost due to exposure: Causal concepts and empirical shortcomings

Excess Years of Life Lost due to exposure is an important measure of health impact complementary to rate or risk statistics. I show that the total excess Years of Life Lost due to exposure can be estimated unbiasedly by calculating the corresponding excess Years of Potential Life Lost given conditions that describe study validity (like exchangeability of exposed and unexposed) and assuming that exposure is never preventive. I further demonstrate that the excess Years of Life Lost conditional on age at death cannot be estimated unbiasedly by a calculation of conditional excess Years of Potential Life Lost without adopting speculative causal models that cannot be tested empirically. Furthermore, I point out by example that the excess Years of Life Lost for a specific cause of death, like lung cancer, cannot be identified from epidemiologic data without assuming non-testable assumptions about the causal mechanism as to how exposure produces death. Hence, excess Years of Life Lost estimated from life tables or regression models, as presented by some authors for lung cancer or after stratification for age, are potentially biased. These points were already made by Robins and Greenland 1991 reasoning on an abstract level. In addition, I demonstrate by adequate life table examples designed to critically discuss the Years of Potential Life Lost analysis published by Park et al. 2002 that the potential biases involved may be fairly extreme. Although statistics conveying information about the advancement of disease onset are helpful in exposure impact analysis and especially worthwhile in exposure impact communication, I believe that attention should be drawn to the difficulties involved and that epidemiologists should always be aware of these conceptual limits of the Years of Potential Life Lost method when applying it as a regular tool in cohort analysis

Diesel exhaust in miners study: how to understand the findings?

Abstract The Diesel Exhaust in Miners Study (DEMS) is an outstanding epidemiological project on the association between occupational diesel exhaust exposures, measured as long-term respirable elemental carbon (REC) estimates, and lung cancer mortality in a large cohort of US miners. Two articles published recently (Attfield et al. (J Natl Cancer Inst Epub, 2012), Silverman et al. (J Natl Cancer Inst Epub, 2012)) dsescribed the epidemiological findings. These papers are expected to have considerable impact on the evaluation of the carcinogenic potential of diesel exhaust and, furthermore, on occupational and environmental limit value discussions related to diesel motor emissions and particle exposures. DEMS found remarkable exposure-response relationships between REC exposure estimates and lung cancer mortality - conditional on a pronounced effect of surface vs. underground work on lung cancer risk. If this risk factor is ignored the estimated REC-lung cancer association is attenuated substantially. The authors relied on this risk factor in their main analyses. However, this factor “surface/underground work” remained unexplained. The factor lead the authors to introduce unusual cross-product terms of location and smoking in adjustment procedures and even caused the authors to hypothesize that high REC exposures are protective against lung cancer excess risks due to smoking. To understand the reliability of these conclusions, we should ask basic questions about the data collection process in DEMS: Did the mortality follow-up procedures suffer from errors like those that affected the NCI formaldehyde cohort study? Are the REC and/or smoking data reliable, and are these data collected/constructed in such a way that the procedures allow valid comparisons between surface and underground workers? Without clarifying the issues raised in this Commentary the Diesel Exhaust in Miners Study remains to be difficult to interpret.</p

Ableitung kumulativer Dosismodelle zur Auswertung physischer Belastungen

Zusammenfassung Hintergrund Als vertiefende Reanalyse der Deutschen Wirbelsäulenstudie (DWS1) versucht die DWS-Richtwertestudie die Ableitung von kumulativen Dosismodellen, mit denen der Dosis-Wirkung-Zusammenhang zwischen bandscheibenbedingten Erkrankungen an der Lendenwirbelsäule und physischen Belastungen im Sinne der Berufskrankheit Nr. 2108 der Berufskrankheiten-Verordnung (BKV) für Männer und Frauen beschrieben werden kann. Drei besondere Herausforderungen ergeben sich für die statistische Analyse: 1. Dosimetrie: Das optimale Dosismodell ist unbekannt. Kandidatenmodelle variieren weit mit angesetzten „Schwellen“ für die Rumpfvorneigung, die Lendenwirbelsäulen-Bandscheiben-Druckkraft und die Tagesdosis. (Erst ab der jeweiligen Schwelle werden die Belastungsanteile bei der Dosisbestimmung mit quadratischer Kraftwichtung relativ zur Vorgangsdauer und „Vollwertberücksichtigung“ der Bandscheibendruckkraft einbezogen.) 2. Epidemiologie: Die Form des optimalen Risikomodells [stetige „Odds-ratio“(OR)-Kurve] über der Lebensdosis ist unbekannt und kann komplex sein (z. B. „W-Form“); dies führt zu einer großen Zahl an Kandidatenmodellen. 3. Richtwertermittlung: Auf Basis der Risikoanalyse soll geprüft werden, ob eine Verdopplungsdosis mit 95 %-Konfidenzintervall abgeleitet werden kann, also die Lebensdosis, die mit einer Verdopplung des Erkrankungsrisikos einhergeht. Material und Methoden Es wurde eine 2-schrittige Multi-Modell-Analyse (MMA) mit informationsgestützter Modellmittelung durchgeführt. Zu allen sinnvollen Kombinationen der Dosismodelleigenschaften werden als Risikomodelle multivariable fraktionale Polynome (FP) 2. und 4. Grades berechnet. Deren Anpassungsgüte wird mit dem Akaike-Informations-Kriterium (AIC) gemessen. Im 1. Schritt der MMA werden für jede Eigenschaft separat optimale Schwellenwerte durch gewichtete Mittelung bestimmt; hierbei wird als Gewicht der relative Informationsgehalt des jeweiligen FP verwendet (Akaike-Gewicht). Diese optimalen Schwellenwerte werden dann eingesetzt, um ein Kombinationsdosismodell zu ermitteln, dessen Werte als Referenzdosis (Bezugsabszisse) für alle weiteren Analysen dienen. Über dieser Referenzdosis werden in einem 2. Schritt der MMA alle kontinuierlichen Regressionskurven und deren Konfidenzbänder Akaike-gewichtet gemittelt (Mittelung der Modellprädiktoren für jedes Individuum) und auf ein OR = 1 bei einer Dosis = 0 bezogen. Die so ermittelten OR-Schätzer werden mit FP 4. Grades geglättet, um finale stetige OR-Kurven über der Referenzdosis mit 95 %-Konfidenzintervallen zu erhalten. Aus diesen Kurven werden durch Umkehrung die Verdopplungsdosis und ein zugehöriges Konfidenzintervall geschätzt. Ergebnisse Die MMA wird auf alle 4 Fallgruppen (FG; 2 Geschlechter, 2 Endpunkte: Prolaps und Chondrose) erfolgreich angewendet. Die Methodik wird an FG 1 (Männer/Prolaps) demonstriert. Schlussfolgerung Bei fehlenden oder strittigen biologischen A-priori-Kriterien zur Spezifizierung des optimalen dosimetrischen oder epidemiologischen Modells stellt die MMA einen Lösungsweg dar. </jats:sec

The impact of performance communication on process efficiency : a RPA implementation project

How can you use an effective and reproducible process to incentivise executives of one of Portugal's largest companies to prioritise a repetitive task? This question led to our main goal of the project, which is documented in the following and supported by fundamental literature research, how to design and implement effective performance-based communication through the technological mapping of Robotic Process Automation. The problem of the Master Thesis was brought to the author's attention by the research partnership of the Portuguese Postal Service CTT with the Catolica Lisbon School of Business and Economics and was attempted to be solved in an implementation project with the help of the already existing RPA capabilities of CTT. In a first step, the processes affected by the problem were documented and subsequently restructured with the help of the CTT project management framework in order to integrate them into the architecture of the organisation using the RPA technology. In order to solve the problem of incentivising higher prioritisation of the task, the nudging method of communication was identified. By applying this, personalised ranking-based notifications were sent to solve the problem. In summary, the underlying work should provide the reader with documentation to apply the described process in a replicable way to comparable problems.Como pode utilizar um processo eficaz e reprodutível para incentivar os executivos de uma das maiores empresas portuguesas a darem prioridade a uma tarefa repetitiva? Esta pergunta conduziu ao nosso principal objectivo do projecto, documentado no seguinte e apoiado por investigação bibliográfica fundamental, como conceber e implementar uma comunicação eficaz baseada no desempenho através do mapeamento tecnológico da Automação de Processos Robóticos. O problema da tese de mestrado foi trazido à atenção do autor pela parceria de investigação do Serviço Postal Português CTT com a Escola de Negócios e Economia Católica de Lisboa e foi tentado ser resolvido num projecto de implementação com a ajuda das capacidades de RPA já existentes dos CTT. Numa primeira fase, os processos afectados pelo problema foram documentados e subsequentemente reestruturados com a ajuda do quadro de gestão do projecto dos CTT, a fim de os integrar na arquitectura da organização utilizando a tecnologia RPA. A fim de resolver o problema de incentivar a atribuição de maior prioridade à tarefa, foi identificado o método de comunicação de incentivo. Ao aplicar este método, foram enviadas notificações personalizadas baseadas na classificação para resolver o problema. Em resumo, o trabalho subjacente deveria fornecer ao leitor documentação para aplicar o processo descrito de uma forma replicável a problemas comparáveis

Attributing the burden of cancer at work: three areas of concern when examining the example of shift-work

This commentary intends to instigate discussions about epidemiologic estimates and their interpretation of attributable fractions (AFs) and the burden of disease (BOD) of cancers due to factors at workplaces. By examining recent work that aims to estimate the number of cancers attributable to shift-work in Britain, we suggest that (i) causal, (ii) practical and (iii) methodological areas of concern may deter us from attributable caseload estimations of cancers at this point in time. Regarding (i), such calculations may have to be avoided as long as we lack established causality between shift-work and the development of internal cancers. Regarding (ii), such calculations may have to be avoided as long as we can neither abandon shift-work nor identify personnel that may be unaffected by shift-work factors. Regarding (iii), there are at least four methodological pitfalls which are likely to make AF calculations uninterpretable at this stage. The four pitfalls are: (1) The use of Levin's 1953 formula in case of adjusted relative risks; (2) The use of broad definitions of exposure in calculations of AFs; (3) The non-additivity of AFs across different levels of exposure and covariables; (4) The fact that excess mortality counts are misleading due to the fact that a human being dies exactly once - a death may occur earlier or later, but a death cannot occur more than once nor can it be avoided altogether for any given individual. Overall, causal, practical and methodological areas of concern should be diligently considered when performing and interpreting AF or BOD computations which - at least at the present time - may not be defensible

Bayesian bias adjustments of the lung cancer SMR in a cohort of German carbon black production workers

<p>Abstract</p> <p>Background</p> <p>A German cohort study on 1,528 carbon black production workers estimated an elevated lung cancer SMR ranging from 1.8-2.2 depending on the reference population. No positive trends with carbon black exposures were noted in the analyses. A nested case control study, however, identified smoking and previous exposures to known carcinogens, such as crystalline silica, received prior to work in the carbon black industry as important risk factors.</p> <p>We used a Bayesian procedure to adjust the SMR, based on a prior of seven independent parameter distributions describing smoking behaviour and crystalline silica dust exposure (as indicator of a group of correlated carcinogen exposures received previously) in the cohort and population as well as the strength of the relationship of these factors with lung cancer mortality. We implemented the approach by Markov Chain Monte Carlo Methods (MCMC) programmed in R, a statistical computing system freely available on the internet, and we provide the program code.</p> <p>Results</p> <p>When putting a flat prior to the SMR a Markov chain of length 1,000,000 returned a median posterior SMR estimate (that is, the adjusted SMR) in the range between 1.32 (95% posterior interval: 0.7, 2.1) and 1.00 (0.2, 3.3) depending on the method of assessing previous exposures.</p> <p>Conclusions</p> <p>Bayesian bias adjustment is an excellent tool to effectively combine data about confounders from different sources. The usually calculated lung cancer SMR statistic in a cohort of carbon black workers overestimated effect and precision when compared with the Bayesian results. Quantitative bias adjustment should become a regular tool in occupational epidemiology to address narrative discussions of potential distortions.</p