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Occupational Health Informatics is the field concerned with the optimal use of information to maximize occupational health and well-being, achieved by multidisciplinary professionals using appropriate technologies. The occupational health community should advocate for the recording of occupation in electronic health record systems. Data linkages can be created between occupation, disease, function and exposures to inform occupational functionality, recognition and recording of occupational diseases, disability accommodation and work as a health outcome.

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.

The DNA fingerprinting techniques, 16S-restriction fragment length polymorphism (16S-RFLP), ribosomal intergenic spacer analysis (RISA) and repetitive extragenic palindrome PCR (Rep-PCR), were used for analyzing the bacterial communities of seven pulp and paper wastewater treatment systems. All three methods generate DNA fingerprints that can be compared using the computer-assisted program, Gelcompar©. Community similarity coefficients were based on quantitative determinations of both the positions of the DNA bands and the band intensities in order to compare the relative differences in the populations. Unique 16S-RFLP DNA fingerprints were observed for each mill suggesting that individual mills contained phylogenetically different communities. However this method was not sensitive enough to detect differences within a mill treatment system from different locations or from different sampling times. The RISA method, which generated more complex fingerprints than 16S-RFLP, could, for some mills, discern differences between samples. The Rep-PCR technique, however, showed the highest degree of resolution and produced not only distinct patterns for each mill but also distinct fingerprints for the temporal and spatial samples from some of the treatment systems. The sensitivity of this method might potentially be used to monitor the stability of the bacterial community within a secondary treatment system.

Introduction Information about occupations and job attributes is available in siloed databases. The lack of integrated data precludes ad-hoc querying and research investigating occupational determinants of health e.g. COVID-19 or stress. Core to integration of occupation data is taxonomic representation of job categories. In North America the official occupational taxonomies are the Canadian National Occupational Classification (NOC) and the United States Standard Occupational Classification (SOC). Objectives This study aimed to integrate job attribute data from the Canadian Career Handbook (CH) and O*NET database to facilitate cross-classification query capabilities and to prototype the creation of metrics for comparing occupations based on job attributes. Methods The integrated database was completed hierarchical structures of both occupational taxonomies were represented; job attributes were selected from the CH and O*NET-SOC; the database was populated with occupational descriptions; occupational codes from the CH and O*NET-SOC were linked using the Brookfield Institute NOC to O*NET-SOC crosswalk. Results The database consists of 1679 rows with unique occupations and 181 columns with occupational attributes. Rows contain a unique combination of hierarchical structures from the CH and O*NET-SOC. Rows also contain detailed occupational descriptions from CH and O*NET-SOC. We queried the integrated data checking O*NET-SOC to CH equivalence and cross-taxonomy selection of job attributes, e.g. Retrieve all or selected attributes for an occupation by CH code or equivalent code in O*NET-SOC. We ran queries for targeted scenarios to retrieve occupations: i) where work is done in physical proximity to others, ii) where incumbents are exposed to disease or infections, iii) at risk of back pain due to physical work factors, iv) where incumbents experience high work-related stressors. Conclusion We report a database combining selected information from the CH and O*NET-SOC that facilitates complex occupational health queries. Further we investigated work-related stressors on low back pain risk by occupation.

Introduction Occupational data in prospective cohort studies is often underutilized due to the human and financial resources required to code open-ended text, such as job titles. Recognizing the value of occupational data in health research, as well as potential errors associated with manual coding, an Automated Coding Algorithm (ACA)-NOC algorithm was developed utilizing a Natural Language Processing approach. Objectives We tested the ACA-NOC algorithm on two regional cohorts of a pan-Canadian cohort study, which represents the largest dataset an algorithm of this kind has been applied to. This process will harmonize and greatly expand the utility of the occupational data, enrich the research platforms, and further refine the efficiency of the algorithm. Methods The ACA-NOC algorithm was tested on data from the Canadian Partnership for Tomorrow’s Health (CanPath), a longitudinal cohort examining the role of genetic, environmental, lifestyle, and behavioural factors in the development of cancer and chronic disease. Using an iterative and interactive approach, the algorithm was applied to job title data from 111,000 questionnaires from two regional cohorts, coding the data to the Canadian National Occupation Classification (NOC) system. The algorithm was further refined based on each round of analysis, increasing the quantity of accurately coded data. Results Results from this research demonstrate the ability to refine the ACA-NOC algorithm with a 10% overall improvement in exact matching from the baseline algorithm. There were also instances where the algorithm performance was superior to the manual coding. The utilization of the algorithm offers significant savings in time, human resources and cost compared to a singular manual coding approach. Conclusions The coding and harmonization of this multi-cohort data demonstrates the value of the ACA-NOC algorithm, while increasing the utility of the CanPath data and research related to occupational health. Future research may involve comparisons between CanPath and international cohorts.