Beyond Crosswalks: Reliability of Exposure Assessment Following Automated Coding of Free-Text Job Descriptions for Occupational Epidemiology.
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| Title: | Beyond Crosswalks: Reliability of Exposure Assessment Following Automated Coding of Free-Text Job Descriptions for Occupational Epidemiology. |
|---|---|
| Authors: | Burstyn, Igor1, Slutsky, Anton2, Lee, Derrick G.3, Singer, Alison B.4, An, Yuan2, Michael, Yvonne L.5 |
| Source: | Annals of Occupational Hygiene. May2014, Vol. 58 Issue 4, p482-492. 11p. |
| Subjects: | High performance computing, Algorithms, Job descriptions, Statistics, Occupational hazards, Environmental exposure, Narratives, Medical coding |
| Geographic Terms: | United States |
| Abstract: | Epidemiologists typically collect narrative descriptions of occupational histories because these are less prone than self-reported exposures to recall bias of exposure to a specific hazard. However, the task of coding these narratives can be daunting and prohibitively time-consuming in some settings. The aim of this manuscript is to evaluate the performance of a computer algorithm to translate the narrative description of occupational codes into standard classification of jobs (2010 Standard Occupational Classification) in an epidemiological context. The fundamental question we address is whether exposure assignment resulting from manual (presumed gold standard) coding of the narratives is materially different from that arising from the application of automated coding. We pursued our work through three motivating examples: assessment of physical demands in Women’s Health Initiative observational study, evaluation of predictors of exposure to coal tar pitch volatiles in the US Occupational Safety and Health Administration’s (OSHA) Integrated Management Information System, and assessment of exposure to agents known to cause occupational asthma in a pregnancy cohort. In these diverse settings, we demonstrate that automated coding of occupations results in assignment of exposures that are in reasonable agreement with results that can be obtained through manual coding. The correlation between physical demand scores based on manual and automated job classification schemes was reasonable (r = 0.5). The agreement between predictive probability of exceeding the OSHA’s permissible exposure level for polycyclic aromatic hydrocarbons, using coal tar pitch volatiles as a surrogate, based on manual and automated coding of jobs was modest (Kendall rank correlation = 0.29). In the case of binary assignment of exposure to asthmagens, we observed that fair to excellent agreement in classifications can be reached, depending on presence of ambiguity in assigned job classification (κ = 0.5–0.8). Thus, the success of automated coding appears to depend on the setting and type of exposure that is being assessed. Our overall recommendation is that automated translation of short narrative descriptions of jobs for exposure assessment is feasible in some settings and essential for large cohorts, especially if combined with manual coding to both assess reliability of coding and to further refine the coding algorithm. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 95480168 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Beyond Crosswalks: Reliability of Exposure Assessment Following Automated Coding of Free-Text Job Descriptions for Occupational Epidemiology. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Burstyn%2C+Igor%22">Burstyn, Igor</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Slutsky%2C+Anton%22">Slutsky, Anton</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Lee%2C+Derrick+G%2E%22">Lee, Derrick G.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Singer%2C+Alison+B%2E%22">Singer, Alison B.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22An%2C+Yuan%22">An, Yuan</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Michael%2C+Yvonne+L%2E%22">Michael, Yvonne L.</searchLink><relatesTo>5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Occupational+Hygiene%22">Annals of Occupational Hygiene</searchLink>. May2014, Vol. 58 Issue 4, p482-492. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22High+performance+computing%22">High performance computing</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Job+descriptions%22">Job descriptions</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><br /><searchLink fieldCode="DE" term="%22Narratives%22">Narratives</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+coding%22">Medical coding</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Epidemiologists typically collect narrative descriptions of occupational histories because these are less prone than self-reported exposures to recall bias of exposure to a specific hazard. However, the task of coding these narratives can be daunting and prohibitively time-consuming in some settings. The aim of this manuscript is to evaluate the performance of a computer algorithm to translate the narrative description of occupational codes into standard classification of jobs (2010 Standard Occupational Classification) in an epidemiological context. The fundamental question we address is whether exposure assignment resulting from manual (presumed gold standard) coding of the narratives is materially different from that arising from the application of automated coding. We pursued our work through three motivating examples: assessment of physical demands in Women’s Health Initiative observational study, evaluation of predictors of exposure to coal tar pitch volatiles in the US Occupational Safety and Health Administration’s (OSHA) Integrated Management Information System, and assessment of exposure to agents known to cause occupational asthma in a pregnancy cohort. In these diverse settings, we demonstrate that automated coding of occupations results in assignment of exposures that are in reasonable agreement with results that can be obtained through manual coding. The correlation between physical demand scores based on manual and automated job classification schemes was reasonable (r = 0.5). The agreement between predictive probability of exceeding the OSHA’s permissible exposure level for polycyclic aromatic hydrocarbons, using coal tar pitch volatiles as a surrogate, based on manual and automated coding of jobs was modest (Kendall rank correlation = 0.29). In the case of binary assignment of exposure to asthmagens, we observed that fair to excellent agreement in classifications can be reached, depending on presence of ambiguity in assigned job classification (κ = 0.5–0.8). Thus, the success of automated coding appears to depend on the setting and type of exposure that is being assessed. Our overall recommendation is that automated translation of short narrative descriptions of jobs for exposure assessment is feasible in some settings and essential for large cohorts, especially if combined with manual coding to both assess reliability of coding and to further refine the coding algorithm. [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/meu006 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 482 Subjects: – SubjectFull: High performance computing Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Job descriptions Type: general – SubjectFull: Statistics Type: general – SubjectFull: Occupational hazards Type: general – SubjectFull: Environmental exposure Type: general – SubjectFull: Narratives Type: general – SubjectFull: Medical coding Type: general – SubjectFull: United States Type: general Titles: – TitleFull: Beyond Crosswalks: Reliability of Exposure Assessment Following Automated Coding of Free-Text Job Descriptions for Occupational Epidemiology. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Burstyn, Igor – PersonEntity: Name: NameFull: Slutsky, Anton – PersonEntity: Name: NameFull: Lee, Derrick G. – PersonEntity: Name: NameFull: Singer, Alison B. – PersonEntity: Name: NameFull: An, Yuan – PersonEntity: Name: NameFull: Michael, Yvonne L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 00034878 Numbering: – Type: volume Value: 58 – Type: issue Value: 4 Titles: – TitleFull: Annals of Occupational Hygiene Type: main |
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