Estimated prevalence of exposure to occupational carcinogens in Australia (2011–2012)

Carey, Renee; Driscoll, Timothy; Peters, Susan; Glass, Deborah Catherine; Reid, Alison; Benke, Geza; Fritschi, Lin

doi:10.1136/oemed-2013-101651

Cited by 80 publications

(88 citation statements)

References 30 publications

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“…truck drivers) or industries (e.g. railroad industry), has become more efficient and reproducible with the development of methods that systematically apply programmable exposure decision rules that explicitly link questionnaire response patterns to exposure decisions (Fritschi et al, 2009;Behrens et al, 2012;Pronk et al, 2012;Friesen et al, 2013;Wheeler et al, 2013;Carey et al, 2014;Peters et al, 2014). In addition, we recently demonstrated that we could extract previously made decision rules using nominal classification tree (CT) models that identified patterns between questionnaire responses and expert-based exposure estimates (Wheeler et al, , 2015.…”

Section: Introductionmentioning

confidence: 99%

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study

Friesen

Wheeler

Vermeulen

et al. 2016

ANNHYG

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Section: Introductionmentioning

confidence: 99%

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study

Friesen

Wheeler

Vermeulen

et al. 2016

ANNHYG

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“…We developed these variables as a first step in deriving decision rules that incorporate both the OH information and job-and industry-specific module information in future exposure assessment efforts. In contrast, with the exception of Pronk et al (2012), recently approaches to develop decision rules have focused solely on using the module information (Fritschi et al, 2009;Behrens et al, 2012;MacFarlane et al, 2012;Carey et al, 2014). The derived OH variables can also be used to extract decision rules using statistical learning models, as in Wheeler et al (2013), to improve the transparency of the exposure decision process.…”

Section: Discussionmentioning

confidence: 99%

Systematically Extracting Metal- and Solvent-Related Occupational Information from Free-Text Responses to Lifetime Occupational History Questionnaires

Friesen

Locke

Tornow

et al. 2014

The Annals of Occupational Hygiene

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Objectives: Lifetime occupational history (OH) questionnaires often use open-ended questions to capture detailed information about study participants' jobs. Exposure assessors use this information, along with responses to job-and industry-specific questionnaires, to assign exposure estimates on a job-by-job basis. An alternative approach is to use information from the OH responses and the job-and industry-specific questionnaires to develop programmable decision rules for assigning exposures. As a first step in this process, we developed a systematic approach to extract the free-text OH responses and convert them into standardized variables that represented exposure scenarios.Methods: Our study population comprised 2408 subjects, reporting 11 991 jobs, from a case-control study of renal cell carcinoma. Each subject completed a lifetime OH questionnaire that included verbatim responses, for each job, to open-ended questions including job title, main tasks and activities (task), tools and equipment used (tools), and chemicals and materials handled (chemicals). Based on a review of the literature, we identified exposure scenarios (occupations, industries, tasks/tools/ chemicals) expected to involve possible exposure to chlorinated solvents, trichloroethylene (TCE) in particular, lead, and cadmium. We then used a SAS macro to review the information reported by study participants to identify jobs associated with each exposure scenario; this was done using previously coded standardized occupation and industry classification codes, and a priori lists of associated key words and phrases related to possibly exposed tasks, tools, and chemicals. Exposure variables representing the occupation, industry, and task/tool/chemicals exposure scenarios were added to the work history records of the study respondents. Our identification of possibly TCE-exposed scenarios in the OH responses was compared to an expert's independently assigned probability ratings to evaluate whether we missed identifying possibly exposed jobs.Results: Our process added exposure variables for 52 occupation groups, 43 industry groups, and 46 task/tool/chemical scenarios to the data set of OH responses. Across all four agents, we identified Ann.

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“…In the absence of a gold standard for occupational exposure to shiftwork, we compared the JEM with the exposures assigned by OccIDEAS to individuals from a separate data set, the Australian Work Exposure Study (AWES) 33. AWES was a nation-wide cross-sectional telephone survey investigating the prevalence of current occupational exposure to 38 carcinogens, including shiftwork variables.…”

Section: Methodsmentioning

confidence: 99%

Assessment of exposure to shiftwork mechanisms in the general population: the development of a new job-exposure matrix

et al. 2014

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Estimated prevalence of exposure to occupational carcinogens in Australia (2011–2012)

Cited by 80 publications

References 30 publications

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study

Systematically Extracting Metal- and Solvent-Related Occupational Information from Free-Text Responses to Lifetime Occupational History Questionnaires

Assessment of exposure to shiftwork mechanisms in the general population: the development of a new job-exposure matrix

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