Combining a Job-Exposure Matrix with Exposure Measurements to Assess Occupational Exposure to Benzene in a Population Cohort in Shanghai, China.
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| Title: | Combining a Job-Exposure Matrix with Exposure Measurements to Assess Occupational Exposure to Benzene in a Population Cohort in Shanghai, China. |
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| Authors: | Friesen, Melissa C.1, Coble, Joseph B.1, Lu, Wei2, Shu, Xiao-Ou3, Ji, Bu-Tian1, Xue, Shouzheng1, Portengen, Lutzen4, Chow, Wong-Ho1, Gao, Yu-Tang5, Yang, Gong3, Rothman, Nathaniel1, Vermeulen, Roel1,4 |
| Source: | Annals of Occupational Hygiene. Jan2012, Vol. 56 Issue 1, p80-91. 12p. |
| Subjects: | Benzene, Longitudinal method, Mathematical models, Research funding, Statistics, Occupational hazards, Environmental exposure |
| Geographic Terms: | China |
| Abstract: | Background: Generic job-exposure matrices (JEMs) are often used in population-based epidemiologic studies to assess occupational risk factors when only the job and industry information of each subject is available. JEM ratings are often based on professional judgment, are usually ordinal or semi-quantitative, and often do not account for changes in exposure over time. We present an empirical Bayesian framework that combines ordinal subjective JEM ratings with benzene measurements. Our aim was to better discriminate between job, industry, and time differences in exposure levels compared to using a JEM alone. Methods: We combined 63 221 short-term area air measurements of benzene exposure (1954–2000) collected during routine health and safety inspections in Shanghai, China, with independently developed JEM intensity ratings for each job and industry using a mixed-effects model. The fixed-effects terms included the JEM intensity ratings for job and industry (both ordinal, 0–3) and a time trend that we incorporated as a b-spline. The random-effects terms included job (n = 33) and industry nested within job (n = 399). We predicted the benzene concentration in two ways: (i) a calibrated JEM estimate was calculated using the fixed-effects model parameters for calendar year and JEM intensity ratings; (ii) a job-/industry-specific estimate was calculated using the fixed-effects model parameters and the best linear unbiased predictors from the random effects for job and industry using an empirical Bayes estimation procedure. Finally, we applied the predicted benzene exposures to a prospective population-based cohort of women in Shanghai, China (n = 74 942). Results: Exposure levels were 13 times higher in 1965 than in 2000 and declined at a rate that varied from 4 to 15% per year from 1965 to 1985, followed by a small peak in the mid-1990s. The job-/industry-specific estimates had greater differences between exposure levels than the calibrated JEM estimates (97.5th percentile/2.5th percentile exposure level, BGR95B: 20.4 versus 3.0, respectively). The calibrated JEM and job-/industry-specific estimates were moderately correlated in any given year (Pearson correlation, rp = 0.58). We classified only those jobs and industries with a job or industry JEM exposure probability rating of 3 (>50% of workers exposed) as exposed. As a result, 14.8% of the subjects and 8.7% of the employed person-years in the study population were classified as benzene exposed. The cumulative exposure metrics based on the calibrated JEM and job-/industry-specific estimates were highly correlated (rp = 0.88). Conclusions: We provide a useful framework for combining quantitative exposure data with expert-based exposure ratings in population-based studies that maximized the information from both sources. Our framework calibrated the ratings to a concentration scale between ratings and across time and provided a mechanism to estimate exposure when a job/industry group reported by a subject was not represented in the exposure database. It also allowed the job/industry groups’ exposure levels to deviate from the pooled average for their respective JEM intensity ratings. [ABSTRACT FROM AUTHOR] |
| Copyright of Annals of Occupational Hygiene is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 70438531 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Combining a Job-Exposure Matrix with Exposure Measurements to Assess Occupational Exposure to Benzene in a Population Cohort in Shanghai, China. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Friesen%2C+Melissa+C%2E%22">Friesen, Melissa C.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Coble%2C+Joseph+B%2E%22">Coble, Joseph B.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lu%2C+Wei%22">Lu, Wei</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Shu%2C+Xiao-Ou%22">Shu, Xiao-Ou</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Ji%2C+Bu-Tian%22">Ji, Bu-Tian</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xue%2C+Shouzheng%22">Xue, Shouzheng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Portengen%2C+Lutzen%22">Portengen, Lutzen</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Chow%2C+Wong-Ho%22">Chow, Wong-Ho</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gao%2C+Yu-Tang%22">Gao, Yu-Tang</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Yang%2C+Gong%22">Yang, Gong</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Rothman%2C+Nathaniel%22">Rothman, Nathaniel</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Vermeulen%2C+Roel%22">Vermeulen, Roel</searchLink><relatesTo>1,4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Occupational+Hygiene%22">Annals of Occupational Hygiene</searchLink>. Jan2012, Vol. 56 Issue 1, p80-91. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Benzene%22">Benzene</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+hazards%22">Occupational hazards</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+exposure%22">Environmental exposure</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Generic job-exposure matrices (JEMs) are often used in population-based epidemiologic studies to assess occupational risk factors when only the job and industry information of each subject is available. JEM ratings are often based on professional judgment, are usually ordinal or semi-quantitative, and often do not account for changes in exposure over time. We present an empirical Bayesian framework that combines ordinal subjective JEM ratings with benzene measurements. Our aim was to better discriminate between job, industry, and time differences in exposure levels compared to using a JEM alone. Methods: We combined 63 221 short-term area air measurements of benzene exposure (1954–2000) collected during routine health and safety inspections in Shanghai, China, with independently developed JEM intensity ratings for each job and industry using a mixed-effects model. The fixed-effects terms included the JEM intensity ratings for job and industry (both ordinal, 0–3) and a time trend that we incorporated as a b-spline. The random-effects terms included job (n = 33) and industry nested within job (n = 399). We predicted the benzene concentration in two ways: (i) a calibrated JEM estimate was calculated using the fixed-effects model parameters for calendar year and JEM intensity ratings; (ii) a job-/industry-specific estimate was calculated using the fixed-effects model parameters and the best linear unbiased predictors from the random effects for job and industry using an empirical Bayes estimation procedure. Finally, we applied the predicted benzene exposures to a prospective population-based cohort of women in Shanghai, China (n = 74 942). Results: Exposure levels were 13 times higher in 1965 than in 2000 and declined at a rate that varied from 4 to 15% per year from 1965 to 1985, followed by a small peak in the mid-1990s. The job-/industry-specific estimates had greater differences between exposure levels than the calibrated JEM estimates (97.5th percentile/2.5th percentile exposure level, BGR95B: 20.4 versus 3.0, respectively). The calibrated JEM and job-/industry-specific estimates were moderately correlated in any given year (Pearson correlation, rp = 0.58). We classified only those jobs and industries with a job or industry JEM exposure probability rating of 3 (>50% of workers exposed) as exposed. As a result, 14.8% of the subjects and 8.7% of the employed person-years in the study population were classified as benzene exposed. The cumulative exposure metrics based on the calibrated JEM and job-/industry-specific estimates were highly correlated (rp = 0.88). Conclusions: We provide a useful framework for combining quantitative exposure data with expert-based exposure ratings in population-based studies that maximized the information from both sources. Our framework calibrated the ratings to a concentration scale between ratings and across time and provided a mechanism to estimate exposure when a job/industry group reported by a subject was not represented in the exposure database. It also allowed the job/industry groups’ exposure levels to deviate from the pooled average for their respective JEM intensity ratings. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annals of Occupational Hygiene is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/annhyg/mer080 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 80 Subjects: – SubjectFull: Benzene Type: general – SubjectFull: Longitudinal method Type: general – SubjectFull: Mathematical models Type: general – SubjectFull: Research funding Type: general – SubjectFull: Statistics Type: general – SubjectFull: Occupational hazards Type: general – SubjectFull: Environmental exposure Type: general – SubjectFull: China Type: general Titles: – TitleFull: Combining a Job-Exposure Matrix with Exposure Measurements to Assess Occupational Exposure to Benzene in a Population Cohort in Shanghai, China. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Friesen, Melissa C. – PersonEntity: Name: NameFull: Coble, Joseph B. – PersonEntity: Name: NameFull: Lu, Wei – PersonEntity: Name: NameFull: Shu, Xiao-Ou – PersonEntity: Name: NameFull: Ji, Bu-Tian – PersonEntity: Name: NameFull: Xue, Shouzheng – PersonEntity: Name: NameFull: Portengen, Lutzen – PersonEntity: Name: NameFull: Chow, Wong-Ho – PersonEntity: Name: NameFull: Gao, Yu-Tang – PersonEntity: Name: NameFull: Yang, Gong – PersonEntity: Name: NameFull: Rothman, Nathaniel – PersonEntity: Name: NameFull: Vermeulen, Roel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 00034878 Numbering: – Type: volume Value: 56 – Type: issue Value: 1 Titles: – TitleFull: Annals of Occupational Hygiene Type: main |
| ResultId | 1 |
