Bayesian Hierarchical Modelling of Individual Expert Assessments in the Development of a General-Population Job-Exposure Matrix.
The CANJEM job-exposure matrix compiles expert evaluations of 31 673 jobs from four population-based case–control studies conducted in Montreal. For each job, experts had derived indices of intensity, frequency, and probability of exposure to 258 agents. CANJEM summarizes the exposures assigned to j...
| Publicado en: | Annals of Work Exposures & Health Vol. 64; no. 1; pp. 13 - 25 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141015977&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141015977 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23987308 KJ1E jtl: Annals of Work Exposures & Health issn: 23987308 maglogo: N pubinfo: dt: Jan2020 vid: 64 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 141015977 141015977 141015977 10.1093/annweh/wxz077 141015977 ppf: 13 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Bayesian Hierarchical Modelling of Individual Expert Assessments in the Development of a General-Population Job-Exposure Matrix. aug: au: Sauvé, Jean-François Sylvestre, Marie-Pierre Parent, Marie-Élise Lavoué, Jérôme affil: Department of Environmental and Occupational Health, School of Public Health, Université de Montréal, chemin de la Côte Ste-Catherine, Montréal, Québec, Canada sug: subj: Models, Statistical Methods Occupational Health Occupational Exposure Human Case Control Studies Occupational Hazards Retrospective Design Conceptual Framework Descriptive Statistics ab: The CANJEM job-exposure matrix compiles expert evaluations of 31 673 jobs from four population-based case–control studies conducted in Montreal. For each job, experts had derived indices of intensity, frequency, and probability of exposure to 258 agents. CANJEM summarizes the exposures assigned to jobs into cells defined by occupation/industry, agent, and period. Some cells may, however, be less populated than others, resulting in uncertain estimates. We developed a modelling framework to refine the estimates of sparse cells by drawing on information available in adjacent cells. Bayesian hierarchical logistic and linear models were used to estimate the probability of exposure and the geometric mean (GM) of frequency-weighted intensity (FWI) of cells, respectively. The hierarchy followed the Canadian Classification and Dictionary of Occupations (CCDO) classification structure, allowing for exposure estimates to be provided across occupations (seven-digit code), unit groups (four-digit code), and minor groups (three-digit code). The models were applied to metallic dust, formaldehyde, wood dust, silica, and benzene, and four periods, adjusting for the study from which jobs were evaluated. The models provided estimates of probability and FWI for all cells that pulled the sparsely populated cells towards the average of the higher-level group. In comparisons stratified by cell sample size, shrinkage of the estimates towards the group mean was marked below 5 jobs/cell, moderate from 5 to 9 jobs/cell, and negligible at ≥10 jobs/cell. The modelled probability of three-digit cells were slightly smaller than their descriptive estimates. No systematic trend in between-study differences in exposure emerged. Overall, t he modelling framework for FWI appears to be a suitable approach to refine CANJEM estimates. For probability, the models could be improved by methods better adapted to the large number of cells with no exposure. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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