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Objective Within the scope of the Exposome Project for Health and Occupational Research on applying the exposome concept to working life health, we aimed to provide a broad overview of the status of knowledge on occupational exposures and associated health effects across multiple noncommunicable diseases (NCDs) to help inform research priorities. Methods We conducted a narrative review of occupational risk factors that can be considered to have "consistent evidence for an association," or where there is "limited/inadequate evidence for an association" for 6 NCD groups: nonmalignant respiratory diseases; neurodegenerative diseases; cardiovascular/metabolic diseases; mental disorders; musculoskeletal diseases; and cancer. The assessment was done in expert sessions, primarily based on systematic reviews, supplemented with narrative reviews, reports, and original studies. Subsequently, knowledge gaps were identified, e.g. based on missing information on exposure–response relationships, gender differences, critical time-windows, interactions, and inadequate study quality. Results We identified over 200 occupational exposures with consistent or limited/inadequate evidence for associations with one or more of 60+ NCDs. Various exposures were identified as possible risk factors for multiple outcomes. Examples are diesel engine exhaust and cadmium, with consistent evidence for lung cancer, but limited/inadequate evidence for other cancer sites, respiratory, neurodegenerative, and cardiovascular diseases. Other examples are physically heavy work, shift work, and decision latitude/job control. For associations with limited/inadequate evidence, new studies are needed to confirm the association. For risk factors with consistent evidence, improvements in study design, exposure assessment, and case definition could lead to a better understanding of the association and help inform health-based threshold levels. Conclusions By providing an overview of knowledge gaps in the associations between occupational exposures and their health effects, our narrative review will help setting priorities in occupational health research. Future epidemiological studies should prioritize to include large sample sizes, assess exposures prior to disease onset, and quantify exposures. Potential sources of biases and confounding need to be identified and accounted for in both original studies and systematic reviews. [ABSTRACT FROM AUTHOR]

Objectives Automatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools. Methods We compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools. Results The coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8–58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69–0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61). Conclusions In this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools. [ABSTRACT FROM AUTHOR]

Introduction Retrospective occupational exposure assessment has been challenging in case–control studies in the general population. We aimed to review (i) trends of different assessment methods used in the last 40 years and (ii) evidence of reliability for various assessment methods. Methods Two separate literature reviews were conducted. We first reviewed all general population cancer case–control studies published from 1975 to 2016 to summarize the exposure assessment approach used. For the second review, we systematically reviewed evidence of reliability for all methods observed in the first review. Results Among the 299 studies included in the first review, the most frequently used assessment methods were self-report/assessment (n = 143 studies), case-by-case expert assessment (n = 139), and job-exposure matrices (JEMs; n = 82). Usage trends for these methods remained relatively stable throughout the last four decades. Other approaches, such as the application of algorithms linking questionnaire responses to expert-assigned exposure estimates and modelling of exposure with historical measurement data, appeared in 21 studies that were published after 2000. The second review retrieved 34 comparison studies examining methodological reliability. Overall, we observed slightly higher median kappa agreement between exposure estimates from different expert assessors (~0.6) than between expert estimates and exposure estimates from self-reports (~0.5) or JEMs (~0.4). However, reported reliability measures were highly variable for different methods and agents. Limited evidence also indicates newer methods, such as assessment using algorithms and measurement-calibrated quantitative JEMs, may be as reliable as traditional methods. Conclusion The majority of current research assesses exposures in the population with similar methods as studies did decades ago. Though there is evidence for the development of newer approaches, more concerted effort is needed to better adopt exposure assessment methods with more transparency, reliability, and efficiency. [ABSTRACT FROM AUTHOR]