AbstractPhotoresist materials are indispensable in photolithography, a process used in semiconductor fabrication. The work process and potential hazards in semiconductor production have raised concerns as to adverse health effects. We therefore performed a health risk assessment of occupational exposure to positive photoresists in a single optoelectronic semiconductor factory in Taiwan. Positive photoresists are widely used in the optoelectronic semiconductor industry for photolithography. Occupational exposure was estimated using the Stoffenmanager® model. Bayesian modeling incorporated available personal air sampling data. We examined the composition and by‐products of the photoresists according to descriptions published in the literature and patents; the main compositions assessed were propylene glycol methyl ether acetate (PGMEA), novolac resin, photoactive compound, phenol, cresol, benzene, toluene, and xylene. Reference concentrations for each compound were reassessed and updated if necessary. Calculated hazard quotients were greater than 1 for benzene, phenol, xylene, and PGMEA, indicating that they have the potential for exposures that exceed reference levels. The information from our health risk assessment suggests that benzene and phenol have a higher level of risk than is currently acknowledged. Undertaking our form of risk assessment in the workplace design phase could identify compounds of major concern, allow for the early implementation of control measures and monitoring strategies, and thereby reduce the level of exposure to health risks that workers face throughout their career.
To date, the variant Creutzfeldt‐Jakob disease (vCJD) risk assessments that have been performed have primarily focused on predicting future vCJD cases in the United Kingdom, which underwent a bovine spongiform encephalopathy (BSE) epidemic between 1980 and 1996. Surveillance of potential BSE cases was also used to assess vCJD risk, especially in other BSE‐prevalent EU countries. However, little is known about the vCJD risk for uninfected individuals who accidentally consume BSE‐contaminated meat products in or imported from a country with prevalent BSE. In this article, taking into account the biological mechanism of abnormal prion PrPres aggregation in the brain, the probability of exposure, and the expected amount of ingested infectivity, we establish a stochastic mean exponential growth model of lifetime exposure through dietary intake. Given the findings that BSE agents behave similarly in humans and macaques, we obtained parameter estimates from experimental macaque data. We then estimated the accumulation of abnormal prions to assess lifetime risk of developing clinical signs of vCJD. Based on the observed number of vCJD cases and the estimated number of exposed individuals during the BSE epidemic period from 1980 to 1996 in the United Kingdom, an exposure threshold hypothesis is proposed. Given the age‐specific risk of infection, the hypothesis explains the observations very well from an extreme‐value distribution fitting of the estimated BSE infectivity exposure. The current BSE statistics in the United Kingdom are provided as an example.
Abstract Addressing occupational health and safety concerns early in the design stage anticipates hazards and enables health professionals to recommend control measures that can best protect workers' health. This method is a well-established tool in public health. Importantly, its success depends on a comprehensive exposure assessment that incorporates previous exposure data and outcomes. Traditional methods for characterizing similar occupational exposure scenarios rely on expert judgment or qualitative descriptions of relevant exposure data, which often include undisclosed underlying assumptions about specific exposure conditions. Thus, improved methods for predicting exposure modeling estimates based on available data are needed. This study proposes that cluster analysis can be used to quantify the relevance of existing exposure scenarios that are similar to a new scenario. We demonstrate how this method improves exposure predictions. Exposure data and contextual information of the scenarios were collected from past exposure assessment reports. Prior distributions for the exposure distribution parameters were specified using Stoffenmanager® 8 predictions. Gower distance and k-Medoids clustering algorithm analyses grouped existing scenarios into clusters based on similarity. The information was used in a Bayesian model to specify the degree of correlation between similar scenarios and the scenarios to be assessed. Using the distance metric to characterize the degree of similarity, the performance of the Bayesian model was improved in terms of the average bias of model estimates and measured data, reducing from 0.77 (SD: 2.0) to 0.49 (SD: 1.8). Nevertheless, underestimation of exposures still occurred for some rare scenarios, which tended to be those with highly variable exposure data. In conclusion, the cluster analysis approach may enable transparent selection of similar exposure scenarios for factoring into design-phase assessments and thereby improve exposure modeling estimates.
WOS: 000319871200001 ; PubMed ID: 23198723 ; Aims: Urinary 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) is a widely used biomarker of oxidative stress. However, variability between chromatographic and ELISA methods hampers interpretation of data, and this variability may increase should urine composition differ between individuals, leading to assay interference. Furthermore, optimal urine sampling conditions are not well defined. We performed inter-laboratory comparisons of 8-oxodG measurement between mass spectrometric-, electrochemical- and ELISA-based methods, using common within-technique calibrants to analyze 8-oxodG-spiked phosphate-buffered saline and urine samples. We also investigated human subject- and sample collection-related variables, as potential sources of variability. Results: Chromatographic assays showed high agreement across urines from different subjects, whereas ELISAs showed far more inter-laboratory variation and generally overestimated levels, compared to the chromatographic assays. Excretion rates in timed 'spot' samples showed strong correlations with 24 h excretion (the 'gold' standard) of urinary 8-oxodG (r(p) 0.67-0.90), although the associations were weaker for 8-oxodG adjusted for creatinine or specific gravity (SG). The within-individual excretion of 8-oxodG varied only moderately between days (CV 17% for 24 h excretion and 20% for first void, creatinine-corrected samples). Innovation: This is the first comprehensive study of both human and methodological factors influencing 8-oxodG measurement, providing key information for future studies with this important biomarker. Conclusion: ELISA variability is greater than chromatographic assay variability, and cannot determine absolute levels of 8-oxodG. Use of standardized calibrants greatly improves intra-technique agreement and, for the chromatographic assays, importantly allows integration of results for pooled analyses. If 24 h samples are not feasible, creatinine- or SG-adjusted first morning samples are recommended. ; ECNIS (Environmental Cancer Risk, Nutrition and Individual Susceptibility), a network of excellence operating within the European Union 6th Framework Program, Priority 5:"Food Quality and Safety" [FOOD-CT-2005-513943]; ECNIS2, a coordination and support action within the European Union FP7 Cooperation Theme 2 Food, Agriculture, Fisheries and Biotechnologies; CISBO; Ingeborg; Leo Dannin Foundation; National Science Council, TaiwanNational Science Council of Taiwan [NSC 97-2314-B-040-015-MY3, NSC 100-2628-B-040-001-MY4]; US NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P30ES009089]; Instituto Carlos III division of the Government for Clinical Research [PI-10/00802, RD06/0045/0006]; Generalitat ValencianaGeneralitat Valenciana [ACOM/2012/238]; Swedish Council for Working Life and Social ResearchSwedish Research CouncilSwedish Research Council for Health Working Life & Welfare (Forte); TUBITAK (Technical and Scientific Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108Y049]; Grant Agency of the Czech RepublicGrant Agency of the Czech Republic [P503/11/0084]; Sahlgrenska University Hospital, Gothenburg; UK Medical Research Council via a People Exchange Programme Research Leader Fellowship award [G1001808/98136] ; Some of the authors of this work were partners in, and this work was partly supported by, ECNIS (Environmental Cancer Risk, Nutrition and Individual Susceptibility), a network of excellence operating within the European Union 6th Framework Program, Priority 5:"Food Quality and Safety" (Contract No. FOOD-CT-2005-513943), and also ECNIS 2 , a coordination and support action within the European Union FP7 Cooperation Theme 2 Food, Agriculture, Fisheries and Biotechnologies.; P Moller and S Loft are supported by CISBO and the Ingeborg and Leo Dannin Foundation.; M-R Chao and C-W Hu acknowledge financial support from the National Science Council, Taiwan (Grants NSC 97-2314-B-040-015-MY3 and NSC 100-2628-B-040-001-MY4).; R Santella acknowledges the contribution of Qiao Wang, and support from US NIH P30ES009089.; G Saez and C Cerda acknowledge financial support from the Instituto Carlos III division of the Government for Clinical Research (Grants PI-10/00802 and RD06/0045/0006) and Grant ACOM/2012/238 from Generalitat Valenciana.; K Broberg, C Lindh, and M Hossain acknowledge financial support from the Swedish Council for Working Life and Social Research; H Orhan and N Senduran acknowledge financial support from TUBITAK (Technical and Scientific Research Council of Turkey), grant number 108Y049.; P Rossner, Jr. and RJ Sram acknowledge support from the Grant Agency of the Czech Republic (P503/11/0084).; L Barregard acknowledges financial support from the Sahlgrenska University Hospital, Gothenburg.; MS Cooke acknowledges support from the UK Medical Research Council via a People Exchange Programme Research Leader Fellowship award (G1001808/98136).
